A decomposition method for large-scale sparse coding in representation learning

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2014, Pages 3732-3738

  Li, Yifeng ^a   Caron, Richard J ^b   Ngom, Alioune ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF WINDSOR   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DECOMPOSITION METHODS; OPTIMIZATION METHOD; SPARSE CODING; SPARSE REPRESENTATION;

NEURAL NETWORKS;

EID: 84908474112     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889394     Document Type: Conference Paper

References (19)

1. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.
2. A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images", SIAM Review, vol. 51, no. 1, pp. 34-81, 2009.
3. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York: Springer, 2010.
4. J. Wright, A. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.
5. Y. Li and A. Ngom, "Classification approach based on nonnegative least squares", Neurocomputing, vol. 118, pp. 41-57, 2013.
6. -, "Non-negative least squares for the classification of high dimensional biological data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 447-456, 2013.
7. P. Hoyer, "Modeling receptive fields with non-negative sparse coding", Neurocomputing, vol. 52-54, pp. 547-552, 2003.
8. D. D. Lee and S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature, vol. 401, pp. 788-791, 1999.
9. S. J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "An interior-point method for large-scale l1-regularized least squares", IEEE J. Selected Topics in Signal Processing, vol. 1, no. 4, pp. 606-617, 2007.
10. R. Jenatton, J. Mairal, G. Obozinski, and F. Bach, "Proximal methods for hierarchical sparse coding", Journal of Machine Learning Research, vol. 12, no. 2011, pp. 2297-2334, 2011.
11. Y. Li and A. Ngom, "Sparse representation approaches for the classification of high-dimensional biological data", BMC Systems Biology, vol. 7, no. Suppl 4, p. S6, 2013.
12. H. Lee, A. Battle, R. Raina, and A. Ng, "Efficient sparse coding algorithms", in NIPS. NIPS, December 2006, pp. 801-808.
13. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net", Journal of the Royal Statistical Society - Series B: Statistical Methodology, vol. 67, no. 2, pp. 301-320, 2005.
14. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
15. T. Joachims, "Making large-scale support vector machine learning practical", in Advances in Kernel Methods: Support Vector Learning, B. Scholkopf, C. Burges, and A. Somla, Eds. MIT, 1998, ch. 11, pp. 169-184.
16. D. Bertsekas, Nonlinear Programming, 2nd ed. Belmont, MA: Athena Scientific, 2008.
17. J. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods: Support Vector Learning, B. Scholkopf, C. Burges, and A. Somla, Eds. MIT, 1998, ch. 12, pp. 185-208.
18. N. Dawany, W. Dampier, and A. Tozeren, "Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types", International Journal of Cancer, vol. 128, no. 12, pp. 2881-2891, 2011.
19. Y. Li and A. Ngom, "The sparse representation toolbox in MATLAB", https://sites.google.com/site/sparsereptool.