A fast iterative algorithm for high-dimensional differential network
Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is li...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tang, Zhou [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Computational statistics - Springer Berlin Heidelberg, 1992, 35(2019), 1 vom: 17. Aug., Seite 95-109 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2019 ; number:1 ; day:17 ; month:08 ; pages:95-109 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00180-019-00915-w |
|---|
| Katalog-ID: |
OLC2070887685 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2070887685 | ||
| 003 | DE-627 | ||
| 005 | 20230323142646.0 | ||
| 007 | tu | ||
| 008 | 200820s2019 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00180-019-00915-w |2 doi | |
| 035 | |a (DE-627)OLC2070887685 | ||
| 035 | |a (DE-He213)s00180-019-00915-w-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |a 004 |q VZ |
| 100 | 1 | |a Tang, Zhou |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A fast iterative algorithm for high-dimensional differential network |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag GmbH Germany, part of Springer Nature 2019 | ||
| 520 | |a Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. | ||
| 650 | 4 | |a ADMM | |
| 650 | 4 | |a Differential network | |
| 650 | 4 | |a Gaussian graphical model | |
| 650 | 4 | |a High-dimensional data | |
| 650 | 4 | |a Precision matrix | |
| 700 | 1 | |a Yu, Zhangsheng |4 aut | |
| 700 | 1 | |a Wang, Cheng |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational statistics |d Springer Berlin Heidelberg, 1992 |g 35(2019), 1 vom: 17. Aug., Seite 95-109 |w (DE-627)131054694 |w (DE-600)1104678-8 |w (DE-576)028053559 |x 0943-4062 |7 nnns |
| 773 | 1 | 8 | |g volume:35 |g year:2019 |g number:1 |g day:17 |g month:08 |g pages:95-109 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00180-019-00915-w |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_267 | ||
| 912 | |a GBV_ILN_2018 | ||
| 951 | |a AR | ||
| 952 | |d 35 |j 2019 |e 1 |b 17 |c 08 |h 95-109 | ||
| author_variant |
z t zt z y zy c w cw |
|---|---|
| matchkey_str |
article:09434062:2019----::fsieaieloihfrihiesoadf |
| hierarchy_sort_str |
2019 |
| publishDate |
2019 |
| allfields |
10.1007/s00180-019-00915-w doi (DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p DE-627 ger DE-627 rakwb eng 510 004 VZ Tang, Zhou verfasserin aut A fast iterative algorithm for high-dimensional differential network 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix Yu, Zhangsheng aut Wang, Cheng aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 35(2019), 1 vom: 17. Aug., Seite 95-109 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:35 year:2019 number:1 day:17 month:08 pages:95-109 https://doi.org/10.1007/s00180-019-00915-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 AR 35 2019 1 17 08 95-109 |
| spelling |
10.1007/s00180-019-00915-w doi (DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p DE-627 ger DE-627 rakwb eng 510 004 VZ Tang, Zhou verfasserin aut A fast iterative algorithm for high-dimensional differential network 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix Yu, Zhangsheng aut Wang, Cheng aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 35(2019), 1 vom: 17. Aug., Seite 95-109 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:35 year:2019 number:1 day:17 month:08 pages:95-109 https://doi.org/10.1007/s00180-019-00915-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 AR 35 2019 1 17 08 95-109 |
| allfields_unstemmed |
10.1007/s00180-019-00915-w doi (DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p DE-627 ger DE-627 rakwb eng 510 004 VZ Tang, Zhou verfasserin aut A fast iterative algorithm for high-dimensional differential network 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix Yu, Zhangsheng aut Wang, Cheng aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 35(2019), 1 vom: 17. Aug., Seite 95-109 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:35 year:2019 number:1 day:17 month:08 pages:95-109 https://doi.org/10.1007/s00180-019-00915-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 AR 35 2019 1 17 08 95-109 |
| allfieldsGer |
10.1007/s00180-019-00915-w doi (DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p DE-627 ger DE-627 rakwb eng 510 004 VZ Tang, Zhou verfasserin aut A fast iterative algorithm for high-dimensional differential network 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix Yu, Zhangsheng aut Wang, Cheng aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 35(2019), 1 vom: 17. Aug., Seite 95-109 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:35 year:2019 number:1 day:17 month:08 pages:95-109 https://doi.org/10.1007/s00180-019-00915-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 AR 35 2019 1 17 08 95-109 |
| allfieldsSound |
10.1007/s00180-019-00915-w doi (DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p DE-627 ger DE-627 rakwb eng 510 004 VZ Tang, Zhou verfasserin aut A fast iterative algorithm for high-dimensional differential network 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix Yu, Zhangsheng aut Wang, Cheng aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 35(2019), 1 vom: 17. Aug., Seite 95-109 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:35 year:2019 number:1 day:17 month:08 pages:95-109 https://doi.org/10.1007/s00180-019-00915-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 AR 35 2019 1 17 08 95-109 |
| language |
English |
| source |
Enthalten in Computational statistics 35(2019), 1 vom: 17. Aug., Seite 95-109 volume:35 year:2019 number:1 day:17 month:08 pages:95-109 |
| sourceStr |
Enthalten in Computational statistics 35(2019), 1 vom: 17. Aug., Seite 95-109 volume:35 year:2019 number:1 day:17 month:08 pages:95-109 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Computational statistics |
| authorswithroles_txt_mv |
Tang, Zhou @@aut@@ Yu, Zhangsheng @@aut@@ Wang, Cheng @@aut@@ |
| publishDateDaySort_date |
2019-08-17T00:00:00Z |
| hierarchy_top_id |
131054694 |
| dewey-sort |
3510 |
| id |
OLC2070887685 |
| 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">OLC2070887685</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323142646.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00180-019-00915-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2070887685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00180-019-00915-w-p</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="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fast iterative algorithm for high-dimensional differential network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ADMM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Differential network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gaussian graphical model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-dimensional data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Precision matrix</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Zhangsheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Cheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational statistics</subfield><subfield code="d">Springer Berlin Heidelberg, 1992</subfield><subfield code="g">35(2019), 1 vom: 17. Aug., Seite 95-109</subfield><subfield code="w">(DE-627)131054694</subfield><subfield code="w">(DE-600)1104678-8</subfield><subfield code="w">(DE-576)028053559</subfield><subfield code="x">0943-4062</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:95-109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00180-019-00915-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">08</subfield><subfield code="h">95-109</subfield></datafield></record></collection>
|
| author |
Tang, Zhou |
| spellingShingle |
Tang, Zhou ddc 510 misc ADMM misc Differential network misc Gaussian graphical model misc High-dimensional data misc Precision matrix A fast iterative algorithm for high-dimensional differential network |
| authorStr |
Tang, Zhou |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)131054694 |
| format |
Article |
| dewey-ones |
510 - Mathematics 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0943-4062 |
| topic_title |
510 004 VZ A fast iterative algorithm for high-dimensional differential network ADMM Differential network Gaussian graphical model High-dimensional data Precision matrix |
| topic |
ddc 510 misc ADMM misc Differential network misc Gaussian graphical model misc High-dimensional data misc Precision matrix |
| topic_unstemmed |
ddc 510 misc ADMM misc Differential network misc Gaussian graphical model misc High-dimensional data misc Precision matrix |
| topic_browse |
ddc 510 misc ADMM misc Differential network misc Gaussian graphical model misc High-dimensional data misc Precision matrix |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Computational statistics |
| hierarchy_parent_id |
131054694 |
| dewey-tens |
510 - Mathematics 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Computational statistics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 |
| title |
A fast iterative algorithm for high-dimensional differential network |
| ctrlnum |
(DE-627)OLC2070887685 (DE-He213)s00180-019-00915-w-p |
| title_full |
A fast iterative algorithm for high-dimensional differential network |
| author_sort |
Tang, Zhou |
| journal |
Computational statistics |
| journalStr |
Computational statistics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
95 |
| author_browse |
Tang, Zhou Yu, Zhangsheng Wang, Cheng |
| container_volume |
35 |
| class |
510 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Tang, Zhou |
| doi_str_mv |
10.1007/s00180-019-00915-w |
| dewey-full |
510 004 |
| title_sort |
a fast iterative algorithm for high-dimensional differential network |
| title_auth |
A fast iterative algorithm for high-dimensional differential network |
| abstract |
Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 |
| container_issue |
1 |
| title_short |
A fast iterative algorithm for high-dimensional differential network |
| url |
https://doi.org/10.1007/s00180-019-00915-w |
| remote_bool |
false |
| author2 |
Yu, Zhangsheng Wang, Cheng |
| author2Str |
Yu, Zhangsheng Wang, Cheng |
| ppnlink |
131054694 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00180-019-00915-w |
| up_date |
2024-07-04T02:30:55.068Z |
| _version_ |
1803613902019231744 |
| 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">OLC2070887685</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323142646.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00180-019-00915-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2070887685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00180-019-00915-w-p</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="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fast iterative algorithm for high-dimensional differential network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ADMM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Differential network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gaussian graphical model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-dimensional data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Precision matrix</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Zhangsheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Cheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational statistics</subfield><subfield code="d">Springer Berlin Heidelberg, 1992</subfield><subfield code="g">35(2019), 1 vom: 17. Aug., Seite 95-109</subfield><subfield code="w">(DE-627)131054694</subfield><subfield code="w">(DE-600)1104678-8</subfield><subfield code="w">(DE-576)028053559</subfield><subfield code="x">0943-4062</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:95-109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00180-019-00915-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">08</subfield><subfield code="h">95-109</subfield></datafield></record></collection>
|
| score |
7.4021854 |