Local low-rank matrix approximation

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 741-749

  Lee, Joonseok ^a   Kim, Seungyeon ^a   Lebanon, Guy ^a   Singer, Yoram ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

CLASSICAL APPROACH; LOW-RANK MATRICES; LOW-RANK MATRIX APPROXIMATIONS; MATRIX APPROXIMATION; PREDICTION ACCURACY; TEXT MINING; WEIGHTED SUM;

LEARNING SYSTEMS;

EID: 84897567456     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Bell, R., Koren, Y., and Volinsky, C. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proc. of the ACM SIGKDD Conference, 2007.
2. Billsus, D. and Pazzani, M. J. Learning collaborative information filters. In Proc. of the International Conference on Machine Learning, 1998.
3. Candès, E.J. and Plan, Y. Matrix completion with noise. Proc. of the IEEE, 98(6):925-936, 2010.
4. Candès, E.J. and Tao, T. The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory, 56(5):2053-2080, 2010.
5. DeCoste, D. Collaborative prediction using ensembles of maximum margin matrix factorizations. In Proc. of the International Conference on Machine Learning, 2006.
6. Foygel, R. and Srebro, N. Concentration-based guarantees for low-rank matrix reconstruction. ArXiv Report arXiv.1102.3923, 2011.
7. Foygel, R., Srebro, N., and Salakhutdinov, R. Matrix reconstruction with the local max norm. ArXiv Report arXiv:1210.5196, 2012.
8. Kambhatla, N. and Leen, T. K. Dimension reduction by local principal component analysis. Neural Computation, 9:1493-1516, 1997. (Pubitemid 127462802)
9. Keshavan, R. H., Montanari, A., and Oh, S. Matrix completion from noisy entries. Journal of Machine Learning Research, 99:2057-2078, 2010.
10. Koren, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. of the A CM SIGKDD Conference, 2008.
11. Kumar, S., Mohri, M., and Talwalkar, A. Ensemble nystrom method. In Advances in Neural Information Processing Systems, 2009.
12. Lawrence, N. D. and Urtasun, R. Non-linear matrix factorization with gaussian processes. In Proc. of the International Conference on Machine Learning, 2009.
13. Lee, D. and Seung, H. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems, 2001.
14. Lee, J., Sun, M., Kim, S., and Lebanon, G. Automatic feature induction for stagewise collaborative filtering. In Advances in Neural Information Processing Systems, 2012a.
15. Lee, J., Sun, M., and Lebanon, G. A comparative study of collaborative filtering algorithms. ArXiv Report 1205.3193, 2012b.
16. Lee, J., Sun, M., and Lebanon, G. PREA: Personalized recommendation algorithms toolkit. Journal of Machine Learning Research, 13:2699-2703, 2012c.
17. Mackey, L. W., Talwalkar, A. S., and Jordan, M. I. Divide-and-conquer matrix factorization. In Advances in Neural Information Processing Systems, 2011.
18. Mirbakhsh, N. and Ling, C. X. Clustering-based matrix factorization. ArXiv Report arXiv:1301.6659, 2013.
19. Rennie, J.D.M. and Srebro, N. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of the International Conference on Machine Learning, 2005.
20. Roweis, S. and Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science, 290: 2323-2326, 2000. (Pubitemid 32041578)
21. Salakhutdinov, R. and Mnih, A. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, 2008a.
22. Salakhutdinov, R. and Mnih, A. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proc. of the International Conference on Machine Learning, 2008b.
23. Shalev-Shwartz, S., Gonen, A., and Shamir, O. Large-scale convex minimization with a low-rank constraint. In Proc. of the International Conference on Machine Learning, 2011.
24. Sill, J., Takacs, G., Mackey, L., and Lin, D. Feature-weighted linear stacking. Arxiv preprint arXiv:0911.0460, 2009.
25. Toh, K.C. and Yun, S. An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems. Pacific Journal of Optimization, 6(15):615-640, 2010.
26. Wand, M. P. and Jones, M. C. Kernel Smoothing. Chapman and Hall/CRC, 1995.
27. Wang, Y., Szlam, A., and Lerman, G. Robust locally linear analysis with applications to image denoising and blind inpainting. SIAM Journal on Imaging Sciences, 6(1):526-562, 2013.