Variational bayesian view of weighted trace norm regularization for matrix factorization

IEEE Signal Processing Letters

Volumn 20, Issue 3, 2013, Pages 261-264

  Kim, Yong Deok ^a   Choi, Seungjin ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Collaborative prediction; matrix completion; matrix factorization; trace norm regularization; variational Bayesian inference

Indexed keywords

COLLABORATIVE PREDICTIONS; MATRIX COMPLETION; MATRIX FACTORIZATIONS; TRACE-NORMS; VARIATIONAL BAYESIAN INFERENCES;

BAYESIAN NETWORKS; INFERENCE ENGINES;

PARAMETERIZATION;

EID: 84873675681     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2013.2242468     Document Type: Article

References (16)

1. F. R. Bach, "Consistency of trace norm minimization," J.Mach. Learn. Res., vol. 8, pp. 1019-1048, 2008.
2. E. Candès and T. Tao, "The power of convex relaxation: Near-optimal matrix completion," IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2053-2080, 2009.
3. M. Fazel, "Matrix Rank Minimization With Applications," Ph.D. dissertation, Stanford Univ., Stanford, CA, USA, 2002.
4. R. Foygel, R. Salakhutdinov, O. Shamir, and N. Srebro, "Learning with the weighted trace-norm under arbitrary sampling distributions," in Advances in Neural Information Processing Systems (NIPS). Cambridge, MA, USA: MIT Press, 2011, vol. 24.
5. A. Ilin and T. Raiko, "Practical approaches to principal component analysis in the presence of missing values," J. Mach. Learn. Res., vol. 11, pp. 1957-2000, 2010.
6. R. H. Keshavan, A. Montanari, and S. Oh, "Matrix completion from noisy entries," J. Mach. Learn. Res., vol. 11, pp. 2057-2078, 2010.
7. Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," IEEE Comput., vol. 42, no. 8, pp. 30-37, 2009.
8. Y. J. Lim and Y. W. Teh, "Variational Bayesian approach to movie rating prediction," in Proc. KDD Cup and Workshop, San Jose, CA, 2007.
9. S. Nakajima and M. Sugiyama, "Implicit regularization in variational Bayesian matrix factorization," in Proc. Int. Conf. on Mach. Learning (ICML), Haifa, Israel, 2010.
10. I. Pilászy, D. Zibriczky, and D. Tikk, "Fast ALS-based matrix factorization for explicit and implicit feedback datasets," in Proc. ACM Int. Conf. on Recommender Syst. (RecSys), Barcelona, Spain, 2010.
11. B. Recht, "A simpler approach to matrix completion," J. Mach. Learn. Res., vol. 12, pp. 3413-3430, 2011.
12. R. Salakhutdinov and N. Srebro, "Collaborative filtering in a non-uniform world: Learning with weighted trace norm," in Advances in Neural Information Processing Systems (NIPS). Cambridge,MA, USA: MIT Press, 2010, vol. 23.
13. N. Srebro, J. D.M. Rennie, andT. Jaakkola, "Maximum-marginmatrix factorization," in Advances in Neural Information Processing Systems (NIPS). Cambridge, MA, USA: MIT Press, 2005, vol. 17.
14. N. Srebro and A. Shraibman, "Rank, trace-norm and max-norm," in Proc. of the Annu. Conf. Learning Theory (COLT), Bertinoro, Italy, 2005.
15. G. Takács, I. Pilászy, B. Németh, and D. Tikk, "Scalable collaborative filtering approaches for large recommender systems," J. Mach. Learn. Res., vol. 10, pp. 623-656, 2009.
16. J.Yoo and S. Choi, "Bayesian matrix co-factorization:Variational algorithm and Cramér-Rao bound," in Proc. Eur. Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Athens, Greece, 2011.