Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification
Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF mode...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Feng, Xiaodong [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
|---|
| Schlagwörter: |
Maintenance activities identification |
|---|
| Umfang: |
7 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2016 ; pages:161-167 ; extent:7 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.engappai.2016.02.016 |
|---|
| Katalog-ID: |
ELV029895391 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV029895391 | ||
| 003 | DE-627 | ||
| 005 | 20230625175814.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2016 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.engappai.2016.02.016 |2 doi | |
| 028 | 5 | 2 | |a GBVA2016016000011.pica |
| 035 | |a (DE-627)ELV029895391 | ||
| 035 | |a (ELSEVIER)S0952-1976(16)30036-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 004 | |
| 082 | 0 | 4 | |a 004 |q DE-600 |
| 082 | 0 | 4 | |a 540 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.00 |2 bkl | ||
| 100 | 1 | |a Feng, Xiaodong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| 264 | 1 | |c 2016transfer abstract | |
| 300 | |a 7 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. | ||
| 520 | |a Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. | ||
| 650 | 7 | |a Maintenance activities identification |2 Elsevier | |
| 650 | 7 | |a Non-negative matrix factorization |2 Elsevier | |
| 650 | 7 | |a Semi-supervised learning |2 Elsevier | |
| 650 | 7 | |a PHM data challenge |2 Elsevier | |
| 650 | 7 | |a Label consistent regularization |2 Elsevier | |
| 700 | 1 | |a Jiao, Yuting |4 oth | |
| 700 | 1 | |a Lv, Chuan |4 oth | |
| 700 | 1 | |a Zhou, Dong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Liu, Xiang ELSEVIER |t Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |d 2015 |d the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control |g Amsterdam [u.a.] |w (DE-627)ELV013402978 |
| 773 | 1 | 8 | |g volume:52 |g year:2016 |g pages:161-167 |g extent:7 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.engappai.2016.02.016 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.00 |j Medizin: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 52 |j 2016 |h 161-167 |g 7 | ||
| 953 | |2 045F |a 004 | ||
| author_variant |
x f xf |
|---|---|
| matchkey_str |
fengxiaodongjiaoyutinglvchuanzhoudong:2016----:aecnitnsmsprienneaiearxatrztofraneac |
| hierarchy_sort_str |
2016transfer abstract |
| bklnumber |
44.00 |
| publishDate |
2016 |
| allfields |
10.1016/j.engappai.2016.02.016 doi GBVA2016016000011.pica (DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Feng, Xiaodong verfasserin aut Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier Jiao, Yuting oth Lv, Chuan oth Zhou, Dong oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:52 year:2016 pages:161-167 extent:7 https://doi.org/10.1016/j.engappai.2016.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 52 2016 161-167 7 045F 004 |
| spelling |
10.1016/j.engappai.2016.02.016 doi GBVA2016016000011.pica (DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Feng, Xiaodong verfasserin aut Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier Jiao, Yuting oth Lv, Chuan oth Zhou, Dong oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:52 year:2016 pages:161-167 extent:7 https://doi.org/10.1016/j.engappai.2016.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 52 2016 161-167 7 045F 004 |
| allfields_unstemmed |
10.1016/j.engappai.2016.02.016 doi GBVA2016016000011.pica (DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Feng, Xiaodong verfasserin aut Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier Jiao, Yuting oth Lv, Chuan oth Zhou, Dong oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:52 year:2016 pages:161-167 extent:7 https://doi.org/10.1016/j.engappai.2016.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 52 2016 161-167 7 045F 004 |
| allfieldsGer |
10.1016/j.engappai.2016.02.016 doi GBVA2016016000011.pica (DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Feng, Xiaodong verfasserin aut Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier Jiao, Yuting oth Lv, Chuan oth Zhou, Dong oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:52 year:2016 pages:161-167 extent:7 https://doi.org/10.1016/j.engappai.2016.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 52 2016 161-167 7 045F 004 |
| allfieldsSound |
10.1016/j.engappai.2016.02.016 doi GBVA2016016000011.pica (DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Feng, Xiaodong verfasserin aut Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier Jiao, Yuting oth Lv, Chuan oth Zhou, Dong oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:52 year:2016 pages:161-167 extent:7 https://doi.org/10.1016/j.engappai.2016.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 52 2016 161-167 7 045F 004 |
| language |
English |
| source |
Enthalten in Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation Amsterdam [u.a.] volume:52 year:2016 pages:161-167 extent:7 |
| sourceStr |
Enthalten in Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation Amsterdam [u.a.] volume:52 year:2016 pages:161-167 extent:7 |
| format_phy_str_mv |
Article |
| bklname |
Medizin: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Maintenance activities identification Non-negative matrix factorization Semi-supervised learning PHM data challenge Label consistent regularization |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| authorswithroles_txt_mv |
Feng, Xiaodong @@aut@@ Jiao, Yuting @@oth@@ Lv, Chuan @@oth@@ Zhou, Dong @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
ELV013402978 |
| dewey-sort |
14 |
| id |
ELV029895391 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV029895391</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625175814.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2016.02.016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016016000011.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV029895391</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(16)30036-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Feng, Xiaodong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Maintenance activities identification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-negative matrix factorization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Semi-supervised learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PHM data challenge</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Label consistent regularization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiao, Yuting</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lv, Chuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Dong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Liu, Xiang ELSEVIER</subfield><subfield code="t">Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation</subfield><subfield code="d">2015</subfield><subfield code="d">the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013402978</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:52</subfield><subfield code="g">year:2016</subfield><subfield code="g">pages:161-167</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.engappai.2016.02.016</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">52</subfield><subfield code="j">2016</subfield><subfield code="h">161-167</subfield><subfield code="g">7</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| author |
Feng, Xiaodong |
| spellingShingle |
Feng, Xiaodong ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| authorStr |
Feng, Xiaodong |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV013402978 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science 540 - Chemistry & allied sciences 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 004 DE-600 540 VZ 610 VZ 44.00 bkl Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization Elsevier |
| topic |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization |
| topic_unstemmed |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization |
| topic_browse |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Maintenance activities identification Elsevier Non-negative matrix factorization Elsevier Semi-supervised learning Elsevier PHM data challenge Elsevier Label consistent regularization |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
y j yj c l cl d z dz |
| hierarchy_parent_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| hierarchy_parent_id |
ELV013402978 |
| dewey-tens |
000 - Computer science, knowledge & systems 540 - Chemistry 610 - Medicine & health |
| hierarchy_top_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV013402978 |
| title |
Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| ctrlnum |
(DE-627)ELV029895391 (ELSEVIER)S0952-1976(16)30036-7 |
| title_full |
Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| author_sort |
Feng, Xiaodong |
| journal |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| journalStr |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
zzz |
| container_start_page |
161 |
| author_browse |
Feng, Xiaodong |
| container_volume |
52 |
| physical |
7 |
| class |
004 004 DE-600 540 VZ 610 VZ 44.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Feng, Xiaodong |
| doi_str_mv |
10.1016/j.engappai.2016.02.016 |
| dewey-full |
004 540 610 |
| title_sort |
label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| title_auth |
Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| abstract |
Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. |
| abstractGer |
Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. |
| abstract_unstemmed |
Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification |
| url |
https://doi.org/10.1016/j.engappai.2016.02.016 |
| remote_bool |
true |
| author2 |
Jiao, Yuting Lv, Chuan Zhou, Dong |
| author2Str |
Jiao, Yuting Lv, Chuan Zhou, Dong |
| ppnlink |
ELV013402978 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.engappai.2016.02.016 |
| up_date |
2024-07-06T22:39:20.511Z |
| _version_ |
1803871123420479488 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV029895391</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625175814.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2016.02.016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016016000011.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV029895391</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(16)30036-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Feng, Xiaodong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Maintenance activities identification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-negative matrix factorization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Semi-supervised learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PHM data challenge</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Label consistent regularization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiao, Yuting</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lv, Chuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Dong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Liu, Xiang ELSEVIER</subfield><subfield code="t">Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation</subfield><subfield code="d">2015</subfield><subfield code="d">the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013402978</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:52</subfield><subfield code="g">year:2016</subfield><subfield code="g">pages:161-167</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.engappai.2016.02.016</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">52</subfield><subfield code="j">2016</subfield><subfield code="h">161-167</subfield><subfield code="g">7</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| score |
7.4007416 |