Citation: | WANG Youhua, ZHANG Yiming, ZHANG Jianqiu, et al., “Greedy Matrix Completion with Fitting Error and Rank Iterative Minimization,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 814-819, 2017, doi: 10.1049/cje.2017.06.013 |
J.-F. Cai, E.J. Candès and Z. Shen, “A singular value thresholding algorithm for matrix completion”, SIAM Journal on Optimization, Vol.20, No.4, pp.1956-1982, 2010.
|
E.J. Candès and B. Recht, “Exact matrix completion via convex optimization”, Foundations of Computational Mathematics, Vol.9, No.6, pp.717-772, 2009.
|
E. Candes and T. Tao, “The power of convex relaxation: Nearoptimal matrix completion”, IEEE Transactions on Information Theory, Vol.56, No.5, pp.2053-2080, 2010.
|
R. Keshavan, A.Montanari and S. Oh, “Matrix completion from a fewentries”, IEEE Transation on Information Theory, Vol.56, No.6, pp.2980-2998, 2010.
|
J.D.M. Rennie and N. Srebro, “Fast maximum margin matrix factorization for collaborative prediction”, Proceedings of the 22nd International Conference on Machine Learning, ser. ICML '05, New York, NY, USA: ACM, pp.713-719, 2005.
|
N. Linial, E. London and Y. Rabinovich, “The geometry of graphs and some of its algorithmic applications”, Combinatorica, Vol.15, No.2, pp.215-245, 1995.
|
Z.-P. Liang, “Spatiotemporal imagingwith partially separable functions”, 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007, ISBI 2007, Arlington: IEEE, pp.988-991, 2007.
|
R. Mazumder, T. Hastie and R. Tibshirani, “Spectral regularization algorithms for learning large incomplete matrices”, Journal of Machine Learning Research, Vol.11, pp.2287-2322, 2010.
|
K.-C. Toh and S. Yun, “An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems”, Pacific Journal of Optimization, Vol.6, No.15, pp.615-640, 2010.
|
S. Ma, D. Goldfarb and L. Chen, “Fixed point and bregman iterative methods for matrix rank minimization”, Mathematical Programming, Vol.128, No.1-2, pp.321-353, 2011.
|
G. Marjanovic and V. Solo, “On lq optimization and matrix completion”, IEEE Transactions on Signal Processing, Vol.60, No.11, pp.5714-5724, 2012.
|
W. Dai, E. Kerman, and O. Milenkovic, “A geometric approach to low-rank matrix completion”, IEEE Transactions on Information Theory, Vol.58, No.1, pp.237-247, 2012.
|
R.H. Keshavan and S. Oh, “Optspace: A gradient descent algorithm on the grassman manifold for matrix completion”, arXiv preprint arXiv:0910.5260, 2009.
|
S.D. Babacan, M. Luessi, R. Molina and A.K. Katsaggelos, “Sparse bayesian methods for low-rank matrix estimation”, IEEE Transactions on Signal Processing, Vol.60, No.8, pp.3964-3977, 2012.
|
F. Leger, G. Yu and G. Sapiro, “Efficient matrix completion with gaussian models”, In ICASSP, Prague, pp.1113-1116, 2011.
|
C.S. Zhao Yujuan, Zheng Baoyu, “Projected gradient descent based on soft thresholding in matrix completion”, Journal of Electronics (CHINA), Vol.30, No.6, pp.517-524, 2013.
|
D.T. Tianyi Zhou, “Godec: Randomized low-rank & sparse matrix decomposition in noisy case”, Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, pp.101-104, 2011.
|
R.H. Keshavan, A. Montanari and S. Oh, “Matrix completion from noisy entries”, Journal of Machine Learning Research, Vol.11, pp.2057-2078, 2010.
|
A. Beck and M. Teboulle, “A fast iterative shrinkagethresholding algorithm for linear inverse problems”, SIAM Journal on Imaging Sciences, Vol.2, No.1, pp.183-202, 2009.
|
A.M.Z.Z. Lu, “Generalized bayesian information criterion for source enumeration in array processing”, IEEE Transactions on Signal Processing, Vol.61, No.6, pp.1470-1480, 2013.
|
S. Friedland, A. Niknejad, M. Kaveh, and H. Zare, “Fast montecarlo low rank approximations for matrices”, System of Systems Engineering, 2006 IEEE/SMC International Conference on, Los Angeles: IEEE, pp.6-10, 2006.
|
K. Goldberg, T. Roeder, D. Gupta and C. Perkins, “Eigentaste: A constant time collaborative filtering algorithm”, Information Retrieval, Vol.4, No.2, pp.133-151, 2001.
|