Robust discrete matrix completion

Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013

Volumn , Issue , 2013, Pages 424-430

  Huang, Jin ^a   Nie, Feiping ^a   Huang, Heng ^a  

^a University of Texas at Arlington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC CONVERGENCE; AUGMENTED LAGRANGIAN METHODS; DISCRETE CONSTRAINTS; DISCRETE STRUCTURE; INTEGER PROGRAMMING PROBLEMS; MATRIX COMPLETION; PREDICTION ACCURACY; THRESHOLD PARAMETERS;

ARTIFICIAL INTELLIGENCE; FORECASTING; INTEGER PROGRAMMING; LAGRANGE MULTIPLIERS;

HEURISTIC METHODS;

EID: 84893418200     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Bertsekas, D. P. 2003. Nonlinear Programming. Athena Scientific.
2. Bertsimas, D. P., and Weismantel, R. 2005. Optimization Over Integers. Belmont, Massachusetts: Dynamic Ideas.
3. Billsus, D., and Pazzani, M. J. 1998. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning, 46-54.
4. Bishop, C. M. 1999. Bayesian pca. In Proceedings of the 1998 conference on Advances in Neural Information Processing Systems, 382-388.
5. Breese, J. S.; Heckerman, D.; and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 43-52.
6. Cai, J.; Candes, E. J.; and Shen, Z. 2010. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization 20(4):1956-1982.
7. Candes, E. J., and Recht, B. 2009. Exact matrix completion via convex optimization. Foundations of Computational Mathematics 9(6):717-772.
8. Chistov, A. L., and Grigoriev, D. Y. 1984. Complexity of quantifier elimination in the theory of algebraically closed fields. Mathematical Foundations of Computer Science, volume 176 of Lecture Notes in Computer Science 17-31.
9. Collins, M.; Dasgupta, S.; and Schapire, R. E. 2001. A generalization of principal component analysis to the exponential family. In Proceedings of Advances in Neural Information Processing Systems, 617-624.
10. de Brevern, A. G.; Hazout, S.; and Malpertuy, A. 2004. Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering. BMC Bioinformatics 5(114).
11. Dewester, S.; Dumains, S.; Landauer, T.; Furnas, G.; and Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the Society for Information Science 41(6):391-407.
12. Fazel, M. 2002. Matrix rank minimization with applications.
13. Hofmann, T. 2001. Learning what people (don?t) want. In Proceedings of the 12th European Conference on Machine Learning, 214-225.
14. Ji, S., and Ye, J. 2009. An accelerated gradient method for trace norm minimization. In Proceedings of the 26th Annual International Conference on Machine Learning, 457-464.
15. Larsen, R. M. 2005. Propackcsoftware for large and sparse svd calculations. available online. url http://sun.stanford.edu/rmunk/propack.
16. Marlin, B. 2003. Modeling user rating profiles for collaborative filtering. In Proceedings of advances in Neural Information Processing Systems, 627-634.
17. Nie, F.; Wang, H.; Cai, X.; Huang, H.; and Ding, C. 2012. Robust matrix completion via joint schatten p-norm and lp- norm minimization. In IEEE 12th International Conference on Data Mining (ICDM), 566-574.
18. Nie, F.; Huang, H.; and Ding, C. 2012. Efficient schatten-p norm minimization for low-rank matrix recovery. In Twenty- Sixth AAAI Conference on Artificial Intelligence (AAAI), 655-661.
19. Oba, S.; Sato, M.; Takemasa, I.; Monden, M.; Matsubara, K.; and Ishii, S. 2003. A bayesian missing value estimation method. Bioinformatics 19(16):2088-2096.
20. Rennie, J. D., and Srebro, N. 2005. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd international conference on Machine learning, 713-719.
21. Salakhutdinov, R., and Mnih, A. 2008. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th International Conference on Machine learning, 880-887.
22. Srebro, N., and Jaakkola, T. 2003. Weighted low-rank approximations. In Proceeding of 20th International Conference on Machine Learning, 720-727.
23. Stark, C.; Breitkreutz, B. J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; and Tyers, M. 2006. Biogrid: a general reprository for interaction datasets. Nucleic Acids Res. 34(Database Issue).