`a`
Big Data and Information Analytics (BDIA)
 

An evolutionary multiobjective method for low-rank and sparse matrix decomposition
Pages: 23 - 37, Issue 1, January 2017

doi:10.3934/bdia.2017006      Abstract        References        Full text (772.1K)           Related Articles

Tao Wu - School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an Shaanxi, 710072, China (email)
Yu Lei - School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an Shaanxi, 710072, China (email)
Jiao Shi - School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an Shaanxi, 710072, China (email)
Maoguo Gong - School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an Shaanxi, 710072, China (email)

1 A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, 2 (2009), 183-202.       
2 J.-F. Cai, E. J. Candès and Z. Shen, A singular value thresholding algorithm for matrix completion, SIAM Journal on Optimization, 20 (2010), 1956-1982.       
3 Z. Cai and Y. Wang, A multiobjective optimization-based evolutionary algorithm for constrained optimization, IEEE Transactions on Evolutionary Computation, 10 (2006), 658-675.
4 E. J. Candès, X. Li, Y. Ma and J. Wright, Robust principal component analysis?, Journal of the ACM (JACM), 58 (2011), Art. 11, 37 pp.       
5 E. J. Candès and B. Recht, Exact matrix completion via convex optimization, Foundations of Computational Mathematics, 9 (2009), 717-772.       
6 V. Chandrasekaran, S. Sanghavi, P. A. Parrilo and A. S. Willsky, Rank-sparsity incoherence for matrix decomposition, SIAM Journal on Optimization, 21 (2011), 572-596.       
7 C. A. C. Coello, D. A. Van Veldhuizen and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Genetic Algorithms and Evolutionary Computation, 5. Kluwer Academic/Plenum Publishers, New York, 2002.       
8 K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Ltd., Chichester, 2001.       
9 K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6 (2002), 182-197.
10 M. Fazel, H. Hindi and S. P. Boyd, A rank minimization heuristic with application to minimum order system approximation, in Proceedings of the American Control Conference, IEEE, 6 (2001), 4734-4739.
11 M. Gong, L. Jiao, H. Du and L. Bo, Multiobjective immune algorithm with nondominated neighbor-based selection, Evolutionary Computation, 16 (2008), 225-255.
12 Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen and Y. Ma, Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix, Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), vol. 61, 2009.
13 Z. Lin, M. Chen and Y. Ma, The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices, arXiv preprint, arXiv:1009.5055, 2010.
14 K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer Academic Publishers, Boston, MA, 1999.       
15 C. Qian, Y. Yu and Z.-H. Zhou, Pareto ensemble pruning, in AAAI, (2015), 2935-2941.
16 --, Subset selection by pareto optimization, in Advances in Neural Information Processing Systems, (2015), 1774-1782.
17 B. Recht, M. Fazel and P. A. Parrilo, Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization, SIAM Review, 52 (2010), 471-501.       
18 J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in Proceedings of the 1st international Conference on Genetic Algorithms. L. Erlbaum Associates Inc., (1985), 93-100.
19 J. L. Starck, M. Elad and D. L. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Transactions on Image Processing, 14 (2005), 1570-1582.       
20 J. Yan, J. Liu, Y. Li, Z. Niu and Y. Liu, Visual saliency detection via rank-sparsity decomposition, in IEEE International Conference on Image Processing, IEEE, (2010), 1089-1092.
21 X. Yuan and J. Yang, Sparse and low-rank matrix decomposition via alternating direction methods, Pacific Journal of Optimization, 9 (2013), 167-180.       
22 C. Zhang, J. Liu, Q. Tian, C. Xu, H. Lu, and S. Ma, Image classification by non-negative sparse coding, low-rank and sparse decomposition, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2011), 1673-1680.
23 Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11 (2007), 712-731.
24 M. Zibulevsky and B. A. Pearlmutter, Blind source separation by sparse decomposition in a signal dictionary, Neural Computation, 13 (2001), 863-882.
25 E. Zitzler, M. Laumanns and L. Thiele et al., SPEA2: Improving the strength pareto evolutionary algorithm, in Eurogen, 3242 (2001), 95-100.

Go to top