Sparse regularization for semi-supervised classification

Pattern Recognition

Volumn 44, Issue 8, 2011, Pages 1777-1784

  Fan, Mingyu ^a   Gu, Nannan ^b   Qiao, Hong ^b   Zhang, Bo ^a  

^a INSTITUTE OF APPLIED MATHEMATICS   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Dimensionality reduction; Regularization theory; Regularized least square classification; Semi supervised learning

Indexed keywords

ADJACENCY GRAPHS; DATA MANIFOLDS; DATA POINTS; DIMENSIONALITY REDUCTION; GEOMETRIC STRUCTURE; INPUT PARAMETER; LEAST SQUARE; LINEAR RECONSTRUCTION; NEW APPROACHES; PENALTY TERM; REAL WORLD DATA; REGULARIZATION ALGORITHMS; REGULARIZATION FRAMEWORK; REGULARIZATION THEORY; REGULARIZED LEAST SQUARE CLASSIFICATION; REGULARIZED LEAST SQUARES; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; SPARSE REPRESENTATION;

COST FUNCTIONS; LEARNING ALGORITHMS;

SUPERVISED LEARNING;

EID: 79953034462     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.02.013     Document Type: Article

References (24)

1. E. Amaldi, and V. Kann On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems Theor. Comput. Sci. 209 1998 237 260 (Pubitemid 128450440)
2. A. Barron, J. Rissanen, and B. Yu The minimum description length principle in coding and modeling IEEE Trans. Inf. Theory 44 1998 2743 2760 (Pubitemid 128741906)
3. M. Belkin, and P. Niyogi Semi-supervised learning on Riemannian manifolds Mach. Learn. 56 13 2004 209 239
4. M. Belkin, V. Sindhwani, and P. Niyogi Manifold regularization: a geometric framework for learning from labeled and unlabeled examples J. Mach. Learn. Res. 7 2006 2399 2434 (Pubitemid 44708005)
5. S. Boyd, and L. Vandenberghe Convex Optimization 2004 Cambridge University Press Cambridge, UK
6. E. Candes, J. Romberg, and T. Tao Stable signal recovery from incomplete and inaccurate measurements Comm. Pure Appl. Math. 59 8 2006 1207 1223 (Pubitemid 43988295)
7. E. Candes, and T. Tao Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52 12 2006 5406 5425 (Pubitemid 46445381)
8. 1-magic: recovery of sparse signals via convex programming, 2005, 〈 http://www.acm.caltech.edu/l1magic/ downloads/l1magic.pdf 〉.
9. X. Cao, H. Qiao, and J. Keane A low-cost pedestrian-detection system with a single optical camera IEEE Trans. Intelligent Transport. Syst. 9 1 2008 58 67 (Pubitemid 351337431)
10. Z. Chen, and S. Haykin On different facets of regularization theory Neural Comput. 14 12 2002 2791 2846
11. CBCL Face Database 1, MIT Center For Biological and Computation Learning, 〈 http://www.ai.mit.edu/projects/cbcl 〉.
12. D. Donoho For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution Comm. Pure Appl. Math. 59 6 2006 797 829 (Pubitemid 43667226)
13. A.S. Georghiades, P.N. Belhumeur, and 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 2001 643 660 (Pubitemid 32585276)
14. F. Girosi, M. Jones, and T. Poggio Regularization theory and neural networks architectures Neural Comput. 7 2 1995 219 269
15. J.J. Hull A database for handwritten text recognition research IEEE Trans. Pattern Anal. Mach. Intell. 16 5 1998 550 554
16. K.C. Lee, J. Ho, and D. Kriegman Acquiring linear subspaces for face recognition under variable lighting IEEE Trans. Pattern Anal. Mach. Intell. 27 5 2005 684 698 (Pubitemid 40608402)
17. T. Poggio, and S. Smale The mathematics of learning: dealing with data Not. Am. Math. Soc. 50 5 2003 537 544
18. A.N. Tikhonov Regularization of incorrectly posed problems Sov. Math. Dokl. 4 1963 1624 1627
19. V.N. Vapnik Statistical Learning Theory 1998 Wiley New York
20. F. Wang, and C. Zhang Robust self-tuning semi-supervised learning Neurocomputing 70 1618 2007 2931 2939 (Pubitemid 47308608)
21. J. Wright, A. Yang, A. Ganesh, S.S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Trans. Pattern Anal. Mach. Intell. 31 2009 210 227
22. J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan Sparse representation for computer vision and pattern recognition Proceedings of the IEEE 98 6 2010 1031 1044
23. H. Xue, S. Chen, and Q. Yang Discriminatively regularized least-squares classification Pattern Recognition 42 1 2009 93 104
24. X. Zhu, Z. Ghahramani, and J. Lafferty Semi-supervised learning using Gaussian fields and harmonic functions The 20th International Conference on Machine Learning (ICML) 2003 912 919