Online dictionary learning for sparse coding

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Mairal, Julien ^a   Bach, Francis ^a   Ponce, Jean ^b   Sapiro, Guillermo ^c  

^a INRIA   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

^c University of Minnesota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS SETS; BATCH ALGORITHMS; DATA VECTORS; DICTIONARY LEARNING; LARGE DATASETS; LINEAR COMBINATIONS; MACHINE-LEARNING; NATURAL IMAGES; ONLINE DICTIONARIES; ONLINE OPTIMIZATION; SPARSE CODING; STOCHASTIC APPROXIMATIONS; TRAINING SAMPLE;

APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; CONVERGENCE OF NUMERICAL METHODS; EDUCATION; IMAGE PROCESSING; IMAGE RECONSTRUCTION; ROBOT LEARNING; SIGNAL PROCESSING;

LEARNING ALGORITHMS;

EID: 70049111613     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553463     Document Type: Conference Paper

References (26)

1. Aharon, M., & Elad, M. (2008). Sparse and redundant modeling of image content using an image-signature-dictionary. SIAM Imaging Sciences, 1, 228-247.
2. Aharon, M., Elad, M., & Bruckstein, A. M. (2006). The KSVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Transactions Signal Processing, 54, 4311-4322
3. Bertsekas, D. (1999). Nonlinear programming. Athena Scientific Belmont, Mass.
4. Bickel, P., Ritov, Y., & Tsybakov, A. (2007). Simultaneous analysis of Lasso and Dantzig selector. preprint.
5. Bonnans, J., & Shapiro, A. (1998). Optimization problems with perturbation: A guided tour. SIAM Review, 40, 202-227.
6. Borwein, J., & Lewis, A. (2006). Convex analysis and non-linear optimization: theory and examples. Springer.
7. Bottou, L. (1998). Online algorithms and stochastic approximations. In D. Saad (Ed.), Online learning and neural networks.
8. Bottou, L., & Bousquet, O. (2008). The tradeoffs of large scale learning. Advances in Neural Information Processing Systems, 20, 161-168.
9. Chen, S., Donoho, D., & Saunders, M. (1999). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20, 33-61.
10. Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407-499.
11. Elad,M., &Aharon,M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions Image Processing, 54, 3736-3745.
12. Fisk, D. (1965). Quasi-martingale. Transactions of the American Mathematical Society, 359-388.
13. Friedman, J., Hastie, T., Hölfling, H., & Tibshirani, R. (2007). Pathwise coordinate optimization. Annals of Statistics, 1, 302-332.
14. Fu, W. (1998). Penalized Regressions: The Bridge Versus the Lasso. Journal of computational and graphical statistics, 7, 397-416.
15. Fuchs, J. (2005). Recovery of exact sparse representations in the presence of bounded noise. IEEE Transactions Information Theory, 51, 3601-3608.
16. Lee, H., Battle, A., Raina, R., & Ng, A. Y. (2007). Efficient sparse coding algorithms. Advances in Neural Information Processing Systems, 19, 801-808.
17. Mairal, J., Elad, M., & Sapiro, G. (2008). Sparse representation for color image restoration. IEEE Transactions Image Processing, 17, 53-69.
18. Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2009). Supervised dictionary learning. Advances in Neural Information Processing Systems, 21, 1033-1040.
19. Mallat, S. (1999). A wavelet tour of signal processing, second edition. Academic Press, New York.
20. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37, 3311-3325.
21. Osborne, M., Presnell, B., & Turlach, B. (2000). A new approach to variable selection in least squares problems. IMA Journal of Numerical Analysis, 20, 389-403.
22. Protter, M., & Elad, M. (2009). Image sequence denoising via sparse and redundant representations. IEEE Transactions Image Processing, 18, 27-36.
23. Raina, R., Battle, A., Lee, H., Packer, B., & Ng, A. Y. (2007). Self-taught learning: transfer learning from unlabeled data. Proceedings of the 26th International Conference on Machine Learning, 759-766.
24. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B, 67, 267-288.
25. Van der Vaart, A. (1998). Asymptotic Statistics. Cambridge University Press.
26. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B, 67, 301-320.