Proximal methods for sparse hierarchical dictionary learning

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 487-494

  Jenatton, Rodolphe ^a   Mairal, Julien ^a   Obozinski, Guillaume ^a   Bach, Francis ^a  

^a Centre National de la Recherche Scientifique   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DICTIONARY LEARNING; HIERARCHICAL STRUCTURES; MODELING DATA; NATURAL IMAGES; PRIMAL-DUAL METHODS; PROBABILISTIC TOPIC MODELS; PROXIMAL METHODS; SPARSE REPRESENTATION; TEXT DOCUMENT;

SIGNAL PROCESSING;

LEARNING SYSTEMS;

EID: 77956506018     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Bach, F. High-dimensional non-linear variable selection through hierarchical kernel learning. Technical report, arXiv.0909.0844, 2009.
2. Beck, A. and Teboulle, M. A fast iterative shrinkage- thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci., 2(1): 183-202, 2009.
3. Bengio, S., Pereira, F., Singer, Y., and Strelow, D. Group sparse coding. In Adv. NIPS, 2009.
4. Bengio, Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 2009.
5. Bertsekas, D. P. Nonlinear programming. Athena Scientific Belmont, 1999.
6. Blei, D., Ng, A., and Jordan, M. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
7. Blei, D. and McAuliffe, J. Supervised topic models. In Adv. NIPS, 2008.
8. Blei, D., Griffiths, T and Jordan, M. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 2010.
9. Boyd, S. P. and Vandenberghe, L. Convex optimization. Cambridge University Press, 2004.
10. Buntine, W.L. Variational Extensions to EM and Multinomial PCA. In Proc. ECML, 2002.
11. Elad, M. and Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process., 54(12):3736-3745, 2006. (Pubitemid 44811686)
12. Jacob, L., Obozinski, G. and Vert, J.-P. Group Lasso with overlap and graph Lasso. In Proc. ICML, 2009.
13. Jenatton, R., Audibert, J.-Y. and Bach, F. Structured variable selection with sparsity-inducing norms. Technical report, arXiv-.0904.3523, 2009.
14. Jenatton, R., Obozinski, G. and Bach, F. Structured sparse principal component analysis. In Proc. AISTATS, 2010.
15. Ji, S., and Ye, J. An accelerated gradient method for trace norm minimization. In Proc. ICML, 2009.
16. Kavukcuoglu, K., Ranzato, M., Fergus, R. and LeCun, Y. Learning invariant features through topographic filter maps. In Proc. CVPR, 2009.
17. Kim, S. and Xing, E. P. Tree-guided group lasso for multitask regression with structured sparsity. Technical report, arXiv:0909.1373, 2009.
18. Lee, D. D. and Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788-791, 1999.
19. Lee, H., Battle, A., Raina, R. and Ng, A. Y. Efficient sparse coding algorithms. In Adv. NIPS, 2007.
20. Mairal, J., Bach, F., Ponce, J., Sapiro, G. and Zisserman, A. Supervised Dictionary Learning. In Adv. NIPS, 2009.
21. Mairal, J., Bach, F., Ponce, J. and Sapiro, G. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res., 11, 19-60, 2010.
22. Nesterov, Y. Gradient methods for minimizing composite objective function. Technical report, CORE, 2007.
23. Olshausen, B. A. and Field, D. J. Sparse coding with an overcomplete basis set: A strategy employed by VI? Vision Research, 37:3311-3325, 1997.
24. Zhao, P., Rocha, G. and Yu, B. The composite absolute penalties family for grouped and hierarchical variable selection. Ann. Stat., 37(6A):3468-3497, 2009.
25. Zhu, J., Ahmed, A. and Xing, E. P. MedLDA: maximum margin supervised topic models for regression and classification. In Proc. ICML, 2009.