Learning efficient structured sparse models

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 615-622

  Sprechmann, Pablo ^a   Bronstein, Alex ^a   Sapiro, Guillermo ^b  

^a TEL AVIV UNIVERSITY   (Israel)

^b University of Minnesota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMANTS; FEED FORWARD; LARGE-SCALE APPLICATIONS; OBJECTIVE FUNCTIONS; OPTIMIZATION ALGORITHMS; ORDERS OF MAGNITUDE; PERFORMANCE DEGRADATION; REAL TIME; SPARSE CODES; SPARSE CODING; STANDARD OPTIMIZATION METHOD;

ALGORITHMS; LEARNING SYSTEMS;

ITERATIVE METHODS;

EID: 84867138199     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Bach, F., Jenatton, R., Mairal, J., and Obozinski, G. Convex optimization with sparsity-inducing norms. In Optimization for Machine Learning. MIT Press, 2011.
2. Beck, A. and Teboulle, M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Img. Sci., 2:183-202, March 2009.
3. Chen, S., Donoho, D., and Saunders, M. Atomic decomposition by basis pursuit. SIAM J. Scientific Computing, 20(1):33-61, 1999.
4. Donoho, D. Compressed sensing. IEEE Trans. on Inf. Theory, 52(4):1289-1306, Apr. 2006.
5. Eldar, Y. C. and Rauhut, H. Average case analysis of multichannel sparse recovery using convex relaxation. IEEE Trans. on Inf. Theory, 56(1):505-519, 2010.
6. Friedman, J., Hastie, T., and Tibshirani, R. A note on the group lasso and a sparse group lasso. Preprint, 2010.
7. Goodfellow, I., Le, Q., Saxe, A., Lee, H., and Ng, A. Y. Measuring invariances in deep networks. In In NIPS, pp. 646-654. 2009.
8. Gregor, K. and LeCun, Y. Learning fast approximations of sparse coding. In ICML, pp. 399-406, 2010.
9. Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., and LeCun, Y. What is the best multi-stage architecture for object recognition? In CVPR, pp. 2146-2153, 2009.
10. Jenatton, R., Mairal, J., Obozinski, G., and Bach, F. Proximal methods for hierarchical sparse coding. JMLR, 12: 2297-2334, 2011.
11. Kavukcuoglu, K., Ranzato, M.A., and LeCun, Y. Fast inference in sparse coding algorithms with applications to object recognition. arXiv.1010.3467, 2010.
12. 1 minimization with application to compressed sensing; a greedy algorithm. Inverse Problems and Imaging, 3: 487-503, 2009.
13. Mairal, J., Bach, F., Ponce, J., and Sapiro, G. Online dictionary learning for sparse coding. In ICML, pp. 689-696, 2009.
14. Nesterov, Y. Gradient methods for minimizing composite objective function. In CORE. Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2007.
15. Randen, T. and Husoy, J. H. Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell., 21(4):291-310, 1999.
16. Ranzato, M., Huang, F. J., Boureau, Y-L., and LeCun, Y. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In CVPR, 2007.
17. Sprechmann, P., Ramírez, I., Sapiro, G., and Eldar, Y. C. C-hilasso: A collaborative hierarchical sparse modeling framework. IEEE Trans. Signal Process., 59(9):4183-4198, 2011.
18. Tibshirani, R. Regression shrinkage and selection via the LASSO. J. Royal Stat. Society: Series B, 58(1):267-288, 1996.
19. Tseng, P. Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl., 109(3):475-494, June 2001.
20. Weinberger, K.Q. and Saul, L.K. Distance metric learning for large margin nearest neighbor classification. JMLR, 10:207-244, 2009.
21. Xiang, Z. J., Xu, H., and Ramadge, P. J. Learning sparse representations of high dimensional data on large scale dictionaries. In NIPS, 24:900-908, 2011.
22. Yuan, M. and Lin, Y. Model selection and estimation in regression with grouped variables. J. Royal Stat. Society, Series B, 68:49-67, 2006.
23. Zhao, P., Rocha, G., and Yu, B. The composite absolute penalties family for grouped and hierarchical variable selection. Annals of Statistics, 37(6A):3468, 2009.