Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm
Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based se...
Ausführliche Beschreibung
Autor*in: |
Caijuan Shi [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2015 |
---|
Schlagwörter: |
learning (artificial intelligence) |
---|
Systematik: |
|
---|
Übergeordnetes Werk: |
Enthalten in: IEEE transactions on multimedia - New York, NY : Institute of Electrical and Electronics Engineers, 1999, 17(2015), 1, Seite 16-28 |
---|---|
Übergeordnetes Werk: |
volume:17 ; year:2015 ; number:1 ; pages:16-28 |
Links: |
---|
DOI / URN: |
10.1109/TMM.2014.2375792 |
---|
Katalog-ID: |
OLC1960756486 |
---|
LEADER | 01000caa a2200265 4500 | ||
---|---|---|---|
001 | OLC1960756486 | ||
003 | DE-627 | ||
005 | 20220216154748.0 | ||
007 | tu | ||
008 | 160206s2015 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1109/TMM.2014.2375792 |2 doi | |
028 | 5 | 2 | |a PQ20160617 |
035 | |a (DE-627)OLC1960756486 | ||
035 | |a (DE-599)GBVOLC1960756486 | ||
035 | |a (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 | ||
035 | |a (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q DNB |
084 | |a ST 325: |q AVZ |2 rvk | ||
084 | |a 54.87 |2 bkl | ||
100 | 0 | |a Caijuan Shi |e verfasserin |4 aut | |
245 | 1 | 0 | |a Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
264 | 1 | |c 2015 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
520 | |a Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. | ||
650 | 4 | |a learning (artificial intelligence) | |
650 | 4 | |a Laplace equations | |
650 | 4 | |a Robustness | |
650 | 4 | |a semi-supervised learning | |
650 | 4 | |a image annotation task | |
650 | 4 | |a Educational institutions | |
650 | 4 | |a Hessian regularization | |
650 | 4 | |a Training data | |
650 | 4 | |a L 2 | |
650 | 4 | |a Information science | |
650 | 4 | |a image segmentation | |
650 | 4 | |a Hessian matrices | |
650 | 4 | |a 1/2}} -matrix norm | |
650 | 4 | |a feature extraction | |
650 | 4 | |a iterative methods | |
650 | 4 | |a Manifolds | |
650 | 4 | |a semisupervised learning | |
650 | 4 | |a 1/2 -matrix norm | |
650 | 4 | |a web image annotation | |
650 | 4 | |a iterative algorithm | |
650 | 4 | |a Hessian semisupervised sparse feature selection | |
650 | 4 | |a large-scale Web image | |
650 | 4 | |a Internet | |
650 | 4 | |a l_{2 | |
650 | 4 | |a sparse feature selection | |
650 | 4 | |a Algorithms | |
650 | 4 | |a Drafting | |
650 | 4 | |a Production planning | |
700 | 0 | |a Qiuiqi Ruan |4 oth | |
700 | 0 | |a Gaoyun An |4 oth | |
700 | 0 | |a Ruizhen Zhao |4 oth | |
773 | 0 | 8 | |i Enthalten in |t IEEE transactions on multimedia |d New York, NY : Institute of Electrical and Electronics Engineers, 1999 |g 17(2015), 1, Seite 16-28 |w (DE-627)266019404 |w (DE-600)1467073-2 |w (DE-576)074960644 |x 1520-9210 |7 nnns |
773 | 1 | 8 | |g volume:17 |g year:2015 |g number:1 |g pages:16-28 |
856 | 4 | 1 | |u http://dx.doi.org/10.1109/TMM.2014.2375792 |3 Volltext |
856 | 4 | 2 | |u http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 |
856 | 4 | 2 | |u http://search.proquest.com/docview/1640795292 |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-MAT | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_4318 | ||
936 | r | v | |a ST 325: |
936 | b | k | |a 54.87 |q AVZ |
951 | |a AR | ||
952 | |d 17 |j 2015 |e 1 |h 16-28 |
author_variant |
c s cs |
---|---|
matchkey_str |
article:15209210:2015----::esasmspriesasfaueeetob |
hierarchy_sort_str |
2015 |
bklnumber |
54.87 |
publishDate |
2015 |
allfields |
10.1109/TMM.2014.2375792 doi PQ20160617 (DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Caijuan Shi verfasserin aut Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning Qiuiqi Ruan oth Gaoyun An oth Ruizhen Zhao oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 1, Seite 16-28 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:1 pages:16-28 http://dx.doi.org/10.1109/TMM.2014.2375792 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 1 16-28 |
spelling |
10.1109/TMM.2014.2375792 doi PQ20160617 (DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Caijuan Shi verfasserin aut Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning Qiuiqi Ruan oth Gaoyun An oth Ruizhen Zhao oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 1, Seite 16-28 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:1 pages:16-28 http://dx.doi.org/10.1109/TMM.2014.2375792 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 1 16-28 |
allfields_unstemmed |
10.1109/TMM.2014.2375792 doi PQ20160617 (DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Caijuan Shi verfasserin aut Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning Qiuiqi Ruan oth Gaoyun An oth Ruizhen Zhao oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 1, Seite 16-28 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:1 pages:16-28 http://dx.doi.org/10.1109/TMM.2014.2375792 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 1 16-28 |
allfieldsGer |
10.1109/TMM.2014.2375792 doi PQ20160617 (DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Caijuan Shi verfasserin aut Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning Qiuiqi Ruan oth Gaoyun An oth Ruizhen Zhao oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 1, Seite 16-28 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:1 pages:16-28 http://dx.doi.org/10.1109/TMM.2014.2375792 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 1 16-28 |
allfieldsSound |
10.1109/TMM.2014.2375792 doi PQ20160617 (DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Caijuan Shi verfasserin aut Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning Qiuiqi Ruan oth Gaoyun An oth Ruizhen Zhao oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 1, Seite 16-28 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:1 pages:16-28 http://dx.doi.org/10.1109/TMM.2014.2375792 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 1 16-28 |
language |
English |
source |
Enthalten in IEEE transactions on multimedia 17(2015), 1, Seite 16-28 volume:17 year:2015 number:1 pages:16-28 |
sourceStr |
Enthalten in IEEE transactions on multimedia 17(2015), 1, Seite 16-28 volume:17 year:2015 number:1 pages:16-28 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
IEEE transactions on multimedia |
authorswithroles_txt_mv |
Caijuan Shi @@aut@@ Qiuiqi Ruan @@oth@@ Gaoyun An @@oth@@ Ruizhen Zhao @@oth@@ |
publishDateDaySort_date |
2015-01-01T00:00:00Z |
hierarchy_top_id |
266019404 |
dewey-sort |
14 |
id |
OLC1960756486 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1960756486</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220216154748.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TMM.2014.2375792</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1960756486</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1960756486</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon</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">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 325:</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Caijuan Shi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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="520" ind1=" " ind2=" "><subfield code="a">Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">learning (artificial intelligence)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Laplace equations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robustness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semi-supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image annotation task</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Educational institutions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian regularization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Training data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">L 2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian matrices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">1/2}} -matrix norm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">iterative methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Manifolds</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semisupervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">1/2 -matrix norm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">web image annotation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">iterative algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian semisupervised sparse feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">large-scale Web image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Internet</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">l_{2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drafting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Production planning</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qiuiqi Ruan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gaoyun An</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ruizhen Zhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on multimedia</subfield><subfield code="d">New York, NY : Institute of Electrical and Electronics Engineers, 1999</subfield><subfield code="g">17(2015), 1, Seite 16-28</subfield><subfield code="w">(DE-627)266019404</subfield><subfield code="w">(DE-600)1467073-2</subfield><subfield code="w">(DE-576)074960644</subfield><subfield code="x">1520-9210</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:16-28</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TMM.2014.2375792</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1640795292</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">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">ST 325:</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.87</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">16-28</subfield></datafield></record></collection>
|
author |
Caijuan Shi |
spellingShingle |
Caijuan Shi ddc 004 rvk ST 325: bkl 54.87 misc learning (artificial intelligence) misc Laplace equations misc Robustness misc semi-supervised learning misc image annotation task misc Educational institutions misc Hessian regularization misc Training data misc L 2 misc Information science misc image segmentation misc Hessian matrices misc 1/2}} -matrix norm misc feature extraction misc iterative methods misc Manifolds misc semisupervised learning misc 1/2 -matrix norm misc web image annotation misc iterative algorithm misc Hessian semisupervised sparse feature selection misc large-scale Web image misc Internet misc l_{2 misc sparse feature selection misc Algorithms misc Drafting misc Production planning Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
authorStr |
Caijuan Shi |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)266019404 |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
1520-9210 |
topic_title |
004 DNB ST 325: AVZ rvk 54.87 bkl Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm learning (artificial intelligence) Laplace equations Robustness semi-supervised learning image annotation task Educational institutions Hessian regularization Training data L 2 Information science image segmentation Hessian matrices 1/2}} -matrix norm feature extraction iterative methods Manifolds semisupervised learning 1/2 -matrix norm web image annotation iterative algorithm Hessian semisupervised sparse feature selection large-scale Web image Internet l_{2 sparse feature selection Algorithms Drafting Production planning |
topic |
ddc 004 rvk ST 325: bkl 54.87 misc learning (artificial intelligence) misc Laplace equations misc Robustness misc semi-supervised learning misc image annotation task misc Educational institutions misc Hessian regularization misc Training data misc L 2 misc Information science misc image segmentation misc Hessian matrices misc 1/2}} -matrix norm misc feature extraction misc iterative methods misc Manifolds misc semisupervised learning misc 1/2 -matrix norm misc web image annotation misc iterative algorithm misc Hessian semisupervised sparse feature selection misc large-scale Web image misc Internet misc l_{2 misc sparse feature selection misc Algorithms misc Drafting misc Production planning |
topic_unstemmed |
ddc 004 rvk ST 325: bkl 54.87 misc learning (artificial intelligence) misc Laplace equations misc Robustness misc semi-supervised learning misc image annotation task misc Educational institutions misc Hessian regularization misc Training data misc L 2 misc Information science misc image segmentation misc Hessian matrices misc 1/2}} -matrix norm misc feature extraction misc iterative methods misc Manifolds misc semisupervised learning misc 1/2 -matrix norm misc web image annotation misc iterative algorithm misc Hessian semisupervised sparse feature selection misc large-scale Web image misc Internet misc l_{2 misc sparse feature selection misc Algorithms misc Drafting misc Production planning |
topic_browse |
ddc 004 rvk ST 325: bkl 54.87 misc learning (artificial intelligence) misc Laplace equations misc Robustness misc semi-supervised learning misc image annotation task misc Educational institutions misc Hessian regularization misc Training data misc L 2 misc Information science misc image segmentation misc Hessian matrices misc 1/2}} -matrix norm misc feature extraction misc iterative methods misc Manifolds misc semisupervised learning misc 1/2 -matrix norm misc web image annotation misc iterative algorithm misc Hessian semisupervised sparse feature selection misc large-scale Web image misc Internet misc l_{2 misc sparse feature selection misc Algorithms misc Drafting misc Production planning |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
author2_variant |
q r qr g a ga r z rz |
hierarchy_parent_title |
IEEE transactions on multimedia |
hierarchy_parent_id |
266019404 |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
IEEE transactions on multimedia |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 |
title |
Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
ctrlnum |
(DE-627)OLC1960756486 (DE-599)GBVOLC1960756486 (PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10 (KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon |
title_full |
Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
author_sort |
Caijuan Shi |
journal |
IEEE transactions on multimedia |
journalStr |
IEEE transactions on multimedia |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2015 |
contenttype_str_mv |
txt |
container_start_page |
16 |
author_browse |
Caijuan Shi |
container_volume |
17 |
class |
004 DNB ST 325: AVZ rvk 54.87 bkl |
format_se |
Aufsätze |
author-letter |
Caijuan Shi |
doi_str_mv |
10.1109/TMM.2014.2375792 |
dewey-full |
004 |
title_sort |
hessian semi-supervised sparse feature selection based on } -matrix norm |
title_auth |
Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
abstract |
Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. |
abstractGer |
Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. |
abstract_unstemmed |
Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 |
container_issue |
1 |
title_short |
Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
url |
http://dx.doi.org/10.1109/TMM.2014.2375792 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162 http://search.proquest.com/docview/1640795292 |
remote_bool |
false |
author2 |
Qiuiqi Ruan Gaoyun An Ruizhen Zhao |
author2Str |
Qiuiqi Ruan Gaoyun An Ruizhen Zhao |
ppnlink |
266019404 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth |
doi_str |
10.1109/TMM.2014.2375792 |
up_date |
2024-07-03T22:25:57.669Z |
_version_ |
1803598490681475072 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1960756486</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220216154748.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TMM.2014.2375792</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1960756486</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1960756486</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1502-1283513b49ac80f436fe66918191a3038a55dc79a5e2a0a1601e0e3e58f704d10</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0381447520150000017000100016hessiansemisupervisedsparsefeatureselectionbasedon</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">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 325:</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Caijuan Shi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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="520" ind1=" " ind2=" "><subfield code="a">Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">learning (artificial intelligence)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Laplace equations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robustness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semi-supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image annotation task</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Educational institutions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian regularization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Training data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">L 2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian matrices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">1/2}} -matrix norm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">iterative methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Manifolds</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semisupervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">1/2 -matrix norm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">web image annotation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">iterative algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hessian semisupervised sparse feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">large-scale Web image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Internet</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">l_{2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drafting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Production planning</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qiuiqi Ruan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gaoyun An</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ruizhen Zhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on multimedia</subfield><subfield code="d">New York, NY : Institute of Electrical and Electronics Engineers, 1999</subfield><subfield code="g">17(2015), 1, Seite 16-28</subfield><subfield code="w">(DE-627)266019404</subfield><subfield code="w">(DE-600)1467073-2</subfield><subfield code="w">(DE-576)074960644</subfield><subfield code="x">1520-9210</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:16-28</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TMM.2014.2375792</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6971162</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1640795292</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">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">ST 325:</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.87</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">16-28</subfield></datafield></record></collection>
|
score |
7.401272 |