A max-margin perspective on sparse representation-based classification

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 1217-1224

  Wang, Zhaowen ^a   Yang, Jianchao ^b   Nasrabadi, Nasser ^c   Huang, Thomas ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b ADOBE RESEARCH   (United States)

^c U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING;

CLASSIFICATION PERFORMANCE; CLASSIFICATION RESULTS; DECISION BOUNDARY; DISCRIMINATIVE DICTIONARIES; LOSS FUNCTIONS; MARGIN CLASSIFIERS; STOCHASTIC GRADIENT DESCENT ALGORITHM; VISUAL RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 84898824546     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.154     Document Type: Conference Paper

References (29)

1. M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Proc., 54(11):4311-4322, 2006.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In the 5th Annual Workshop on Computational Learning Theory, pages 144-152, 1992.
3. L. Bottou. Stochastic learning. In O. Bousquet and U. von Luxburg, editors, Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence, LNAI 3176, pages 146-168. Springer Verlag, 2004.
4. D. M. Bradley and J. A. Bagnell. Differential sparse coding. In Adv. NIPS, pages 113-120, 2008.
5. C.-K. Chiang, C.-H. Duan, S.-H. Lai, and S.-F. Chang. Learning component-level sparse representation using histogram information for image classification. In Proc. ICCV, pages 1519-1526, 2011.
6. S. F. Cotter. Sparse representation for accurate classification of corrupted and occluded facial expressions. In Proc. ICASSP, pages 838-841, 2010.
7. C. Domeniconi, J. Peng, and D. Gunopulos. Locally adaptive metric nearest-neighbor classification. IEEE Trans. PAMI, 24(9):1281-1285, 2002.
8. K. Engan, S. O. Aase, and J. Hakon Husoy. Method of optimal directions for frame design. In Proc. ICASSP, pages 2443-2446, 1999.
9. A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI, 23(6):643-660, 2001.
10. Z. Jiang, Z. Lin, and L. S. Davis. Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In Proc. CVPR, pages 1697-1704, 2011.
11. T. Kohonen. Improved versions of learning vector quantization. In IJCNN International Joint Conference on Neural Networks, volume 1, pages 545-550, 1990.
12. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
13. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In Adv. NIPS, pages 801-808, 2007.
14. J. Mairal, F. Bach, and J. Ponce. Task-driven dictionary learning. IEEE Trans. PAMI, 32(4), 2012.
15. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proc. ICML, pages 689-696, 2009.
16. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In Proc. CVPR, pages 1-8, 2008.
17. A. Martinez and R. Benavente. The AR face database. CVC Technical Report 24, 1998.
18. N. Mehta and A. Gray. Sparsity-based generalization bounds for predictive sparse coding. In Proc. ICML, 2013.
19. Q. Qiu, Z. Jiang, and R. Chellappa. Sparse dictionary-based representation and recognition of action attributes. In Proc. ICCV, pages 707-714, 2011.
20. I. Ramirez, P. Sprechmann, and G. Sapiro. Classification and clustering via dictionary learning with structured incoherence and shared features. In Proc. CVPR, pages 3501-3508, 2010.
21. M. Soltanolkotabi and E. J. Candes. A geometric analysis of subspace clustering with outliers. arXiv preprint arXiv: 1112.4258, 2011.
22. V. Vapnik. The nature of statistical learning theory. springer, 1999.
23. K. Q.Weinberger, J. Blitzer, and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. In Adv. NIPS, pages 1473-1480, 2006.
24. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. PAMI, 31(2):210-227, 2009.
25. J. Yang, J.Wang, and T. S. Huang. Learning the sparse representation for classification. In Proc. ICME, pages 1-6, 2011.
26. J. Yang, Z. Wang, Z. Lin, X. Shu, and T. Huang. Bilevel sparse coding for coupled feature spaces. In Proc. CVPR, 2012.
27. M. Yang, L. Zhang, X. Feng, and D. Zhang. Fisher discrimination dictionary learning for sparse representation. In Proc. ICCV, pages 543-550, 2011.
28. L. Zhang, M. Yang, and X. Feng. Sparse representation or collaborative representation: Which helps face recognition? In Proc. ICCV, pages 471-478, 2011.
29. H. Zou, T. Hastie, and R. Tibshirani. On the "degrees of freedom" of the LASSO. The Annals of Statistics, 35(5):2173-2192, 2007.