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
Gespeichert in:
| Autor*in: |
Caijuan Shi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
learning (artificial intelligence) |
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| Systematik: |
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| 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 |
|---|
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OLC1960756486 |
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| 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 | |
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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 |
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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 |
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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 |
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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 |
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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 |
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Hessian Semi-Supervised Sparse Feature Selection Based on } -Matrix Norm |
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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. |
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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. |
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