|
H. Avron, S. Kale, and S. Kasiviswanathan, et al., “Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization,” in Proceedings of the 29th International Conference on Machine Learning, pp. 1231-1238, 2012
|
|
N. Boumal, and P. Absil, “RTRMC: a Riemannian Trust Region Method for Matrix Completion,” in Proceedings of the 25th Annual Conference on Neural Information Processing Systems, Granada, Spain, pp. 406-414, 2011
|
|
M. Brand, “Fast Low-rank Modifications of the Thin Singular Value Decomposition,” Linear Algebra and its Applications, 415(1), pp. 20-30, 2006
|
|
J. F. Cai, E. J. Candès, and Z. Shen, “A Singular Value Thresholding Algorithm for Matrix Completion,” SIAM Journal on optimization, 20(4),pp. 1956-1982, 2010
|
|
E. J. Candès, and T, Tao, “The Power of Convex Relaxation: Near Optimal Matrix Completion,” IEEE Transactions on Information Theory, 56(5), pp. 2053-2080, 2010
|
|
M. Chen, A. Ganesh, Z. Lin, and Y. Ma,etc., “Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-rank Matrix,” Journal of the Marine Biological Association of the Uk, 56(3), pp. 707-722, 2015
|
|
E. Hazan, “Sparse Approximate Solutions to Semidefinite Programs,” in Latin American Conference on Theoretical Informatics, Springer-Verlag, pp. 306-316, 2008
|
|
M. Jaggi, and M Sulovsky, “A Simple Algorithm for Nuclear Norm Regularized Problems,” in International Conference on Machine Learning, pp. 471-478, June 2010
|
|
M. Jamali, and M. Ester, "TrustWalker: a Random Walk Model for Combining Trust-based and Item-based Recommendation," ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397-406, 2009
|
|
F. KLIU, and H. J. LEE, “Use of Social Network Information to Enhance Collaborative Filtering Performance,” Expert Systems with Applications, 37(7), pp. 4772-4778, 2010
|
|
Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model," ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, pp. 426-434, August 2008
|
|
L. Lü, M. Medo, H. Y. Chi, Y. C. Zhang, Z. K. Zhang and T. Zhou, “Recommender Systems,” Physics Reports, 519(1), pp. 1-49, 2012
|
|
S. MA, D. Goldfarb, and L. Chen, “Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization,” Mathematical Programming, 128(1), pp. 321-353, 2011
|
|
A. Mnih, and R. Salakhutdinov, “Probabilistic Matrix Factorization”, Advances in neural information processing systems, pp. 1257-1264, 2007
|
|
Y. G. Peng, J. L. Suo, Q. H. Dai, and W.L, Xu, “From Compressed Sensing to Low-rank Matrix Recovery: Theory and Applications,” Acta Automatica Sinica, 39(7), pp. 981-994, 2013
|
|
N. Srebro, and A. Tewari, “Stochastic Optimization for Machine Learning,” ICML Tutorial, 2010
|
|
G. A. Watson, “Characterization of the Subdifferential of some Matrix Norms,” Linear Algebra and its Applications, 170(0), pp. 33-35, 1992
|
|
Z. W. Wen, W. T. Yin, and Y. Zhang, “Solving a Low-rank Factorization Model for Matrix Completion By a Nonlinear Successive Over-relaxation Algorithm,” Rice University, Technical Report, pp. 1-24, 2010
|
|
J. Yang, X. Yuan, “Linearized Augmented Lagrangian and Alternating Direction Methods for Nuclear Norm Minimization”, Mathematics of Computation, pp. 301-329, 2013
|
|
S. Zhang, W. Wang and J. Ford, “Using Singular Value Decomposition Approximation for Collaborative Filtering”, E-Commerce Technology, pp. 257-264, 2005
|
|
Y. J. Zhao, B. Y. Zheng and S. N. Chen, “Matrix Completion and its Application in Signal Processing,” Journal of Signal processing, pp. 423-436, 2015
|