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Volumn , Issue , 2012, Pages 732-740

Semi-supervised learning with mixed knowledge information

Author keywords

graph laplacian; kernel learning; nuclear norm regularization; pairwise constraints; semi supervised learning (ssl)

Indexed keywords

GRAPH LAPLACIAN; KERNEL LEARNING; NUCLEAR NORM REGULARIZATION; PAIRWISE CONSTRAINTS; SEMI-SUPERVISED LEARNING;

EID: 84866046924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339646     Document Type: Conference Paper
Times cited : (17)

References (43)
  • 2
    • 33745456231 scopus 로고    scopus 로고
    • Tech. rep., Computer Sciences, University of Wisconsin-Madison
    • X. Zhu. Semi-supervised learning literature survey. Tech. rep., Computer Sciences, University of Wisconsin-Madison, 2008.
    • (2008) Semi-supervised Learning Literature Survey
    • Zhu, X.1
  • 3
    • 1942484430 scopus 로고    scopus 로고
    • Semi-supervised learning using Gaussian fields and harmonic functions
    • X. Zhu, Z. Ghahramani, J. Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
    • (2003) ICML , pp. 912-919
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.3
  • 5
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res., 7:2399-2434, 2006.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 6
    • 79955855934 scopus 로고    scopus 로고
    • Laplacian support vector machines trained in the primal
    • S. Melacci, M. Belkin. Laplacian support vector machines trained in the primal. J. Mach. Learn. Res., 12:1149-1184, 2011.
    • (2011) J. Mach. Learn. Res. , vol.12 , pp. 1149-1184
    • Melacci, S.1    Belkin, M.2
  • 7
    • 56449130871 scopus 로고    scopus 로고
    • Pairwise constraint propagation by semidefinite programming for semi-supervised classification
    • Z. Li, J. Liu, X. Tang. Pairwise constraint propagation by semidefinite programming for semi-supervised classification. In ICML, pages 576-583, 2008.
    • (2008) ICML , pp. 576-583
    • Li, Z.1    Liu, J.2    Tang, X.3
  • 8
    • 33144466184 scopus 로고    scopus 로고
    • A discriminative learning framework with pairwise constraints for video object classification
    • R. Yan, J. Zhang, J. Yang, A. Hauptmann. A discriminative learning framework with pairwise constraints for video object classification. IEEE Trans. Pattern Anal. Mach. Intell., 28(4):578-593, 2006.
    • (2006) IEEE Trans. Pattern Anal. Mach. Intell. , vol.28 , Issue.4 , pp. 578-593
    • Yan, R.1    Zhang, J.2    Yang, J.3    Hauptmann, A.4
  • 9
    • 0001898293 scopus 로고    scopus 로고
    • Clustering with instance-level constraints
    • K. Wagstaff, C. Cardie. Clustering with instance-level constraints. In ICML, pages 1103-1110, 2000.
    • (2000) ICML , pp. 1103-1110
    • Wagstaff, K.1    Cardie, C.2
  • 10
    • 9444294778 scopus 로고    scopus 로고
    • From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering
    • D. Klein, S. Kamvar, C. Manning. From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In ICML, pages 307-314, 2002.
    • (2002) ICML , pp. 307-314
    • Klein, D.1    Kamvar, S.2    Manning, C.3
  • 11
    • 12244300524 scopus 로고    scopus 로고
    • A probabilistic framework for semi-supervised clustering
    • S. Basu, M. Bilenko, R. Mooney. A probabilistic framework for semi-supervised clustering. In KDD, pages 59-68, 2004.
    • (2004) KDD , pp. 59-68
    • Basu, S.1    Bilenko, M.2    Mooney, R.3
  • 12
    • 31844447616 scopus 로고    scopus 로고
    • Semi-supervised graph clustering: A kernel approach
    • B. Kulis, S. Basu, I. Dhillon, R. Mooney. Semi-supervised graph clustering: A kernel approach. In ICML, pages 457-464, 2005.
    • (2005) ICML , pp. 457-464
    • Kulis, B.1    Basu, S.2    Dhillon, I.3    Mooney, R.4
  • 13
    • 84879571292 scopus 로고    scopus 로고
    • Distance metric learning, with application to clustering with side-information
    • E. Xing, A. Ng, M. Jordan, S. Russell. Distance metric learning, with application to clustering with side-information. In NIPS, pages 505-512, 2003.
    • (2003) NIPS , pp. 505-512
    • Xing, E.1    Ng, A.2    Jordan, M.3    Russell, S.4
  • 14
    • 14344264451 scopus 로고    scopus 로고
    • Integrating constraints and metric learning in semi-supervised clustering
    • M. Bilenko, S. Basu, R. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In ICML, pages 81-88, 2004.
    • (2004) ICML , pp. 81-88
    • Bilenko, M.1    Basu, S.2    Mooney, R.3
  • 16
    • 1942483137 scopus 로고    scopus 로고
    • Transductive inference for text classification using support vector machines
    • T. Joachims. Transductive inference for text classification using support vector machines. In ICML, pages 200-209, 1999.
    • (1999) ICML , pp. 200-209
    • Joachims, T.1
  • 17
    • 51949086172 scopus 로고    scopus 로고
    • Semi-supervised classification by low density separation
    • O. Chapelle, A. Zien. Semi-supervised classification by low density separation. In AISTATS, pages 57-64, 2005.
    • (2005) AISTATS , pp. 57-64
    • Chapelle, O.1    Zien, A.2
  • 18
    • 79955830804 scopus 로고    scopus 로고
    • A family of simple non-parametric kernel learning algorithms
    • J. Zhuang, I. Tsang, S.C.H. Hoi. A family of simple non-parametric kernel learning algorithms. J. Mach. Learn. Res., 12:1313-1347, 2011.
    • (2011) J. Mach. Learn. Res. , vol.12 , pp. 1313-1347
    • Zhuang, J.1    Tsang, I.2    Hoi, S.C.H.3
  • 19
    • 84899028404 scopus 로고    scopus 로고
    • Nonparametric transforms of graph kernels for semi-supervised learning
    • X. Zhu, J. S. Kandola, Z. Ghahramani, J. D. Lafferty. Nonparametric transforms of graph kernels for semi-supervised learning. In NIPS, pages 1641-1648, 2005.
    • (2005) NIPS , pp. 1641-1648
    • Zhu, X.1    Kandola, J.S.2    Ghahramani, Z.3    Lafferty, J.D.4
  • 20
    • 33749589757 scopus 로고    scopus 로고
    • Learning the unified kernel machines for classification
    • S. Hoi, M. Lyu, E. Chang. Learning the unified kernel machines for classification. In KDD, pages 187-196, 2006.
    • (2006) KDD , pp. 187-196
    • Hoi, S.1    Lyu, M.2    Chang, E.3
  • 21
    • 34547975734 scopus 로고    scopus 로고
    • Learning nonparametric kernel matrices from pairwise constraints
    • S. Hoi, R. Jin, M. Lyu. Learning nonparametric kernel matrices from pairwise constraints. In ICML, pages 361-368, 2007.
    • (2007) ICML , pp. 361-368
    • Hoi, S.1    Jin, R.2    Lyu, M.3
  • 22
    • 78149492457 scopus 로고    scopus 로고
    • Spectral kernel learning for semi-supervised classification
    • W. Liu, B. Qian, J. Cui, J. Liu. Spectral kernel learning for semi-supervised classification. In IJCAI, pages 1150-1155, 2009.
    • (2009) IJCAI , pp. 1150-1155
    • Liu, W.1    Qian, B.2    Cui, J.3    Liu, J.4
  • 23
    • 78149357397 scopus 로고    scopus 로고
    • Semisupervised kernel matrix learning by kernel propagation
    • E. Hu, S. Chen, D. Zhang, X. Yin. Semisupervised kernel matrix learning by kernel propagation. IEEE Trans. Neural Netw., 21(11):1831-1841, 2010.
    • (2010) IEEE Trans. Neural Netw. , vol.21 , Issue.11 , pp. 1831-1841
    • Hu, E.1    Chen, S.2    Zhang, D.3    Yin, X.4
  • 24
    • 84863338208 scopus 로고    scopus 로고
    • Fast graph Laplacian regularized kernel learning via semidefinite- quadratic-linear programming
    • X. -M. Wu, A. So, Z. Li, S. Li. Fast graph Laplacian regularized kernel learning via semidefinite-quadratic-linear programming. In NIPS, pages 1964-1972, 2009.
    • (2009) NIPS , pp. 1964-1972
    • Wu, X.M.1    So, A.2    Li, Z.3    Li, S.4
  • 25
    • 33644783522 scopus 로고    scopus 로고
    • Self-tuning spectral clustering
    • L. Zelnik-Manor, P. Perona. Self-tuning spectral clustering. In NIPS, pages 1601-1608, 2004.
    • (2004) NIPS , pp. 1601-1608
    • Zelnik-Manor, L.1    Perona, P.2
  • 26
    • 36648998944 scopus 로고    scopus 로고
    • Label propagation through linear neighborhoods
    • F. Wang, C. Zhang. Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng., 20(1):55-67, 2008.
    • (2008) IEEE Trans. Knowl. Data Eng. , vol.20 , Issue.1 , pp. 55-67
    • Wang, F.1    Zhang, C.2
  • 27
    • 77951291046 scopus 로고    scopus 로고
    • A singular value thresholding algorithm for matrix completion
    • J. Cai, E. J. Candès, Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM J. Optim., 20(4):1956-1982, 2010.
    • (2010) SIAM J. Optim. , vol.20 , Issue.4 , pp. 1956-1982
    • Cai, J.1    Candès, E.J.2    Shen, Z.3
  • 28
    • 79957957723 scopus 로고    scopus 로고
    • Fixed point and Bregman iterative methods for matrix rank minimization
    • S. Ma, D. Goldfarb, L. Chen. Fixed point and Bregman iterative methods for matrix rank minimization. Math. Program., 128(1):321-353, 2011.
    • (2011) Math. Program. , vol.128 , Issue.1 , pp. 321-353
    • Ma, S.1    Goldfarb, D.2    Chen, L.3
  • 29
    • 80052482844 scopus 로고    scopus 로고
    • The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices
    • submitted
    • Z. Lin, M. Chen, L. Wu. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. Math. Program., submitted, 2009.
    • (2009) Math. Program.
    • Lin, Z.1    Chen, M.2    Wu, L.3
  • 30
    • 71049116435 scopus 로고    scopus 로고
    • Exact matrix completion via convex optimization
    • E. J. Candès, B. Recht. Exact matrix completion via convex optimization. Found. Comput. Math., 9(6):717-772, 2009.
    • (2009) Found. Comput. Math. , vol.9 , Issue.6 , pp. 717-772
    • Candès, E.J.1    Recht, B.2
  • 31
    • 34547972419 scopus 로고    scopus 로고
    • Graph Laplacian regularization for large-scale semidefinite programming
    • K. Q. Weinberger, F. Sha, Q. Zhu, L. K. Saul. Graph Laplacian regularization for large-scale semidefinite programming. In NIPS, pages 1489-1496, 2007.
    • (2007) NIPS , pp. 1489-1496
    • Weinberger, K.Q.1    Sha, F.2    Zhu, Q.3    Saul, L.K.4
  • 32
    • 84863139902 scopus 로고    scopus 로고
    • Learning spectral embedding for semisupervised clustering
    • F. Shang, Y. Liu, F. Wang. Learning spectral embedding for semisupervised clustering. In ICDM, pages 597-606, 2011.
    • (2011) ICDM , pp. 597-606
    • Shang, F.1    Liu, Y.2    Wang, F.3
  • 33
    • 72549110327 scopus 로고    scopus 로고
    • Interior-point method for nuclear norm approximation with application to system identification
    • Z. Liu, L. Vandenberghe. Interior-point method for nuclear norm approximation with application to system identification. SIAM J. Matrix Anal. Appl., 31(3):1235-1256, 2010.
    • (2010) SIAM J. Matrix Anal. Appl. , vol.31 , Issue.3 , pp. 1235-1256
    • Liu, Z.1    Vandenberghe, L.2
  • 34
    • 79951739650 scopus 로고    scopus 로고
    • Robust low-rank subspace segmentation with semidefinite guarantees
    • Y. Ni, J. Sun, X. Yuan, S. Yan, L. Cheong. Robust low-rank subspace segmentation with semidefinite guarantees. In ICDM, pages 1179-1188, 2010.
    • (2010) ICDM , pp. 1179-1188
    • Ni, Y.1    Sun, J.2    Yuan, X.3    Yan, S.4    Cheong, L.5
  • 35
    • 85162074163 scopus 로고    scopus 로고
    • Transduction with matrix completion: Three birds with one stone
    • A. B. Goldberg, X. Zhu, B. Recht, J. Xu, R. Nowak. Transduction with matrix completion: three birds with one stone. In NIPS, 2010.
    • (2010) NIPS
    • Goldberg, A.B.1    Zhu, X.2    Recht, B.3    Xu, J.4    Nowak, R.5
  • 36
    • 0011336092 scopus 로고
    • Characterization of the subdifferential of some matrix norms
    • G. Watson. Characterization of the subdifferential of some matrix norms. Linear Algebra Appl., 170:33-45, 1992.
    • (1992) Linear Algebra Appl. , vol.170 , pp. 33-45
    • Watson, G.1
  • 37
    • 79959639348 scopus 로고    scopus 로고
    • The minimum-rank gram matrix completion via modified fixed point continuation method
    • Y. Ma, L. Zhi. The minimum-rank gram matrix completion via modified fixed point continuation method. In ISSAC, pages 241-248, 2011.
    • (2011) ISSAC , pp. 241-248
    • Ma, Y.1    Zhi, L.2
  • 38
    • 0001531895 scopus 로고
    • Two-point step size gradient methods
    • J. Barzilai, J. Borwein. Two-point step size gradient methods. IMA J. Numer. Anal., 8:141-148, 1988.
    • (1988) IMA J. Numer. Anal. , vol.8 , pp. 141-148
    • Barzilai, J.1    Borwein, J.2
  • 43
    • 0035363672 scopus 로고    scopus 로고
    • From few to many: Illumination cone models for face recognition under variable lighting and pose
    • A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell., 23(6):643-660, 2001.
    • (2001) IEEE Trans. Pattern Anal. Mach. Intell. , vol.23 , Issue.6 , pp. 643-660
    • Georghiades, A.S.1    Belhumeur, P.N.2    Kriegman, D.J.3


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