Dictionary learning for massive matrix factorization

33rd International Conference on Machine Learning, ICML 2016

Volumn 4, Issue , 2016, Pages 2601-2610

  Mensch, Arthur ^a   Mairal, Julien ^b   Thirion, Bertrand ^a   Varoquaux, Gaël ^a  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FACTORIZATION; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING;

COORDINATE DESCENT METHODS; DATA DECOMPOSITION; DICTIONARY LEARNING; FACTORIZATION METHODS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; MATRIX COMPLETION PROBLEMS; MATRIX FACTORIZATIONS; SPARSE MATRIX FACTORIZATION;

MATRIX ALGEBRA;

EID: 84999028733     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Aharon, Michal, Elad, Michael, and Bruckstein, Alfred, k- SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):4311-4322, 2006.
2. Bell, Robert M. and Koren, Yehuda. Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter, 9(2):75-79, 2007.
3. Bingham, Ella and Mannila, Heikki. Random projection in dimensionality reduction: Applications to image and text data. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245-250. ACM, 2001.
4. Blondel, Mathieu, Fujino, Akinori, and Ueda, Naonori. Convex factorization machines. In Machine Learning and Knowledge Discovery in Databases, pp. 19-35. Springer, 2015.
5. Bottou, Leon. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT, pp. 177-186. Springer, 2010.
6. Candes, Emmanuel J. and Recht, Benjamin. Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6):717-772, 2009.
7. Candes, Emmanuel J. and Tao, Terence. Near-optimal signal recovery from random projections: Universal encoding strategies? Information Theory, IEEE Transactions on, 52(12):5406-5425, 2006.
8. Duchi, John, Shalev-Shwartz, Shai, Singer, Yoram, and Chandra, Tushar. Efficient projections onto the 1 1- ball for learning in high dimensions. In Proceedings of the International Conference on Machine Learning, pp. 272-279. ACM, 2008.
9. Halko, Nathan, Martinsson, Per-Gunnar, and Tropp, Joel A. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. arXiv:0909.4061 [math], 2009.
10. Hastie, Trevor, Mazumder, Rahul, Lee, Jason, and Zadeh, Reza. Matrix completion and low-rank SVD via fast alternating least squares. arXiv: 1410.2596 [stat], 2014.
11. Johnson, William B. and Lindenstrauss, Joram. Extensions of Lipschitz mappings into a Hilbert space. Contemporary mathematics, 26(189-206): 1, 1984.
12. Mairal, Julien. Stochastic majorization-minimization algorithms for large-scale optimization. In Advances in Neural Information Processing Systems, pp. 2283-2291, 2013.
13. Mairal, Julien. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision, 8(2-3):85-283, 2014.
14. Mairal, Julien, Bach, Francis, Ponce, Jean, and Sapiro, Guillermo. Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research, 11:19-60, 2010.
15. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J., and Sejnowski, T. J. Analysis of fMRI Data by Blind Separation into Independent Spatial Components. Human Brain Mapping, 6(3): 160- 188, 1998. ISSN1065-9471.
16. Pedregosa, Fabian, Varoquaux, Gael, Gramfort, Alexandre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, Dubourg, Vincent, Vanderplas, Jake, Passos, Alexandre, Cournapeau, David, Brucher, Matthieu, Perrot, Matthieu, and Duchesnay, Edouard. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
17. Pourkamali-Anaraki, Farhad, Becker, Stephen, and Hughes, Shannon M. Efficient dictionary learning via very sparse random projections. In Proceedings of the IEEE International Conference on Sampling Theory and Applications, pp. 478-482. IEEE, 2015.
18. Rendle, Steffen. Factorization machines. In Proceedings of the IEEE International Conference on Data Mining, pp. 995-1000. IEEE, 2010.
19. Rendle, Steffen and Schmidt-Thieme, Lars. Online- updating regularized kernel matrix factorization models for large-scale recommender systems. In Proceedings of the ACM Conference on Recommender systems, pp. 251-258. ACM, 2008.
20. Srebro, Nathan, Rennie, Jason, and Jaakkola, Tommi S. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems, pp. 1329-1336, 2004.
21. Szabo, Zoltan, Poczos, Barnabas, and Lorincz, Andras. Online group-structured dictionary learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2865-2872. IEEE, 2011.
22. Takacs, Gabor, Pilaszy, Istvan, Nemeth, Bottyan, and Tikk, Domonkos. Scalable collaborative filtering approaches for large recommender systems. The Journal of Machine Learning Research, 10:623-656, 2009.
23. Van Essen, David C., Smith, Stephen M., Barch, Deanna M., Behrens, Timothy E. J., Yacoub, Essa, and Ugurbil, Kamil. The WU-Minn Human Connectome Project: An overview. Neurolmage, 80:62-79, 2013.
24. Varoquaux, Gael, Gramfort, Alexandre, Pedregosa, Fabian, Michel, Vincent, and Thirion, Bertrand. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. In Proceedings of the Information Processing in Medical Imaging Conference, volume 22, pp. 562-573. Springer, 2011.
25. Varoquaux, Gael, Schwartz, Yannick, Pinel, Philippe, and Thirion, Bertrand. Cohort-level brain mapping: learning cognitive atoms to single out specialized regions. In Proceedings of the Information Processing in Medical Imaging Conference, pp. 438-449. Springer, 2013.
26. Yu, Hsiang-Fu, Hsieh, Cho-Jui, and Dhillon, Indeijit. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In Proceedings of the International Conference on Data Mining, pp. 765-774. IEEE, 2012.