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January  2020, 16(1): 397-407. doi: 10.3934/jimo.2018159

A $p$-spherical section property for matrix Schatten-$p$ quasi-norm minimization

Yifu Feng 1, and  Min Zhang 2,,

1. 

College of Mathematics, Jilin Normal University, Jilin Siping 136000, China
2. 

School of Mathematical Science, Chongqing Normal University 401131, China, and School of Elec Eng, Comp and Math Sci (EECMS), Curtin University 6102, Australia

* Corresponding author: Min Zhang

Received  February 2016 Revised  August 2017 Published  October 2018

Low-rank matrix recovery has become a popular research topic with various applications in recent years. One of the most popular methods to dual with this problem for overcoming its NP-hardness is to relax it into some tractable optimization problems. In this paper, we consider a nonconvex relaxation, the Schatten-$p$ quasi-norm minimization ($0<p<1$), and discuss conditions for the equivalence between the original problem and this nonconvex relaxation. Specifically, based on null space analysis, we propose a $p$-spherical section property for the exact and approximate recovery via the Schatten-$p$ quasi-norm minimization ($0<p<1$).

Keywords: Low-rank matrix recovery, Schatten-p minimization, spherical section property.
Mathematics Subject Classification: 90C26, 94A12, 90C59.
Citation: Yifu Feng, Min Zhang. A $p$-spherical section property for matrix Schatten-$p$ quasi-norm minimization. Journal of Industrial & Management Optimization, 2020, 16 (1) : 397-407. doi: 10.3934/jimo.2018159
References:
[1]

T. T. Cai and A. Zhang, Sparse representation of a polytope and recovery of sparse signals and low-rank matrices, IEEE Transactions on Information Theory, 60 (2014), 122-132.  doi: 10.1109/TIT.2013.2288639.  Google Scholar

[2]

Y. Chen, S. Bhojanapalli, S. Sanghavi and R. Ward, Coherent matrix completion, in International Conference on Machine Learning, (2014), 674-682. Google Scholar

[3]

A. L. Chistov and D. Yu. Grigoriev, Complexity of quantifier elimination in the theory of algebraically closed fields, in Mathematical Foundations of Computer Science, Lecture Notes in Computer Science, 176 (1984), 17-31. doi: 10.1007/BFb0030287.  Google Scholar

[4]

K. Dvijotham and M. Fazel, Nullspace conditions for the nuclear norm heuristic, In preparation, 2010. Google Scholar

[5]

K. Dvijotham and M. Fazel, A nullspace analysis of the nuclear norm heuristic for rank minimization, in Acoustics Speech and Signal Processing (ICASSP), IEEE, (2010), 3586-3589. doi: 10.1109/ICASSP.2010.5495918.  Google Scholar

[6]

M. Fazel, H. Hindi and S. Boyd, A rank minimization heuristic with application to minimum order system approximation, in Proceedings of the American Control Conference, IEEE, (2001), 4734-4739. doi: 10.1109/ACC.2001.945730.  Google Scholar

[7]

R. H. Keshavan, A. Montanari and S. Oh, Matrix completion from a few entries, IEEE Transactions on Information Theory, 56 (2010), 2980-2998.  doi: 10.1109/TIT.2010.2046205.  Google Scholar

[8]

L. C. Kong, L. Tuncel and N. H. Xiu, $s$-goodness for low-rank matrix recovery, Abstract and Applied Analysis, 2013 (2013), Art. ID 101974, 9 pp.  Google Scholar

[9]

L. C. Kong and N. H. Xiu, Exact low-rank matrix recovery via nonconvex schatten $p$-minimization, Asia-Pacific Journal of Operational Research, 30 (2013), 1340010. doi: 10.1142/S0217595913400101.  Google Scholar

[10]

Y.-F. Li, Y. J. Zhang and Z.-H. Huang, A reweighted nuclear norm minimization algorithm for low rank matrix recovery, Journal of Computational and Applied Mathematics, 263 (2014), 338-350.  doi: 10.1016/j.cam.2013.12.005.  Google Scholar

[11]

L. Lu and R. Vidal, Combined central and subspace clustering for computer vision applications, in Proceedings of the 23rd International Conference on Machine Learning, ACM, 56 (2006), 593-600. doi: 10.1145/1143844.1143919.  Google Scholar

[12]

A. Majumdar and R. K. Ward, An algorithm for sparse MRI reconstruction by Schatten $p$-norm minimization, Magnetic Resonance Imaging, 29 (2011), 408-417.  doi: 10.1016/j.mri.2010.09.001.  Google Scholar

[13]

R. Meka, P. Jain, C. Caramanis and I. S. Dhillon, Rank minimization via online learning, in The International Conference on Machine learning, AMC, (2008), 656-663. doi: 10.1145/1390156.1390239.  Google Scholar

[14]

F. Nie, H. Huang and C.H.F. Ding, An algorithm for sparse MRI reconstruction by Schatten $p$-norm minimization, AAAI, 2012. Google Scholar

[15]

B. Recht, W. Xu and B. Hassibi, Necessary and sufficient condtions for success of the nuclear norm heuristic for rank minimization, in 47th IEEE Conference on Decision and Control, (2008), 3065-3070. Google Scholar

[16]

M.C. Yue and A. M. C. So, A perturbation inequality for concave functions of singular values and its applications in low-rank matrix recovery, Applied and Computational Harmonic Analysis, 40 (2016), 396-416.  doi: 10.1016/j.acha.2015.06.006.  Google Scholar

[17]

M. Zhang, Z. H. Huang and Y. Zhang, Restricted $p$-isometry properties of nonconvex matrix recovery, IEEE Transactions on Information Theory, 59 (2013), 4316-4323.  doi: 10.1109/TIT.2013.2250577.  Google Scholar

[18]

Y. Q. Zhao and J. Yang, Hyperspectral image denoising via sparse representation and low-rank constraint, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 296-308.   Google Scholar

[19]

Z. Zheng, M. Yu, J. Jia, H. Liu, D. Xiang, X. Huang and J. Yang, Fisher discrimination based low rank matrix recovery for face recognition, Pattern Recognition, 47 (2014), 3502-3511.  doi: 10.1016/j.patcog.2014.05.001.  Google Scholar

show all references

References:
[1]

T. T. Cai and A. Zhang, Sparse representation of a polytope and recovery of sparse signals and low-rank matrices, IEEE Transactions on Information Theory, 60 (2014), 122-132.  doi: 10.1109/TIT.2013.2288639.  Google Scholar

[2]

Y. Chen, S. Bhojanapalli, S. Sanghavi and R. Ward, Coherent matrix completion, in International Conference on Machine Learning, (2014), 674-682. Google Scholar

[3]

A. L. Chistov and D. Yu. Grigoriev, Complexity of quantifier elimination in the theory of algebraically closed fields, in Mathematical Foundations of Computer Science, Lecture Notes in Computer Science, 176 (1984), 17-31. doi: 10.1007/BFb0030287.  Google Scholar

[4]

K. Dvijotham and M. Fazel, Nullspace conditions for the nuclear norm heuristic, In preparation, 2010. Google Scholar

[5]

K. Dvijotham and M. Fazel, A nullspace analysis of the nuclear norm heuristic for rank minimization, in Acoustics Speech and Signal Processing (ICASSP), IEEE, (2010), 3586-3589. doi: 10.1109/ICASSP.2010.5495918.  Google Scholar

[6]

M. Fazel, H. Hindi and S. Boyd, A rank minimization heuristic with application to minimum order system approximation, in Proceedings of the American Control Conference, IEEE, (2001), 4734-4739. doi: 10.1109/ACC.2001.945730.  Google Scholar

[7]

R. H. Keshavan, A. Montanari and S. Oh, Matrix completion from a few entries, IEEE Transactions on Information Theory, 56 (2010), 2980-2998.  doi: 10.1109/TIT.2010.2046205.  Google Scholar

[8]

L. C. Kong, L. Tuncel and N. H. Xiu, $s$-goodness for low-rank matrix recovery, Abstract and Applied Analysis, 2013 (2013), Art. ID 101974, 9 pp.  Google Scholar

[9]

L. C. Kong and N. H. Xiu, Exact low-rank matrix recovery via nonconvex schatten $p$-minimization, Asia-Pacific Journal of Operational Research, 30 (2013), 1340010. doi: 10.1142/S0217595913400101.  Google Scholar

[10]

Y.-F. Li, Y. J. Zhang and Z.-H. Huang, A reweighted nuclear norm minimization algorithm for low rank matrix recovery, Journal of Computational and Applied Mathematics, 263 (2014), 338-350.  doi: 10.1016/j.cam.2013.12.005.  Google Scholar

[11]

L. Lu and R. Vidal, Combined central and subspace clustering for computer vision applications, in Proceedings of the 23rd International Conference on Machine Learning, ACM, 56 (2006), 593-600. doi: 10.1145/1143844.1143919.  Google Scholar

[12]

A. Majumdar and R. K. Ward, An algorithm for sparse MRI reconstruction by Schatten $p$-norm minimization, Magnetic Resonance Imaging, 29 (2011), 408-417.  doi: 10.1016/j.mri.2010.09.001.  Google Scholar

[13]

R. Meka, P. Jain, C. Caramanis and I. S. Dhillon, Rank minimization via online learning, in The International Conference on Machine learning, AMC, (2008), 656-663. doi: 10.1145/1390156.1390239.  Google Scholar

[14]

F. Nie, H. Huang and C.H.F. Ding, An algorithm for sparse MRI reconstruction by Schatten $p$-norm minimization, AAAI, 2012. Google Scholar

[15]

B. Recht, W. Xu and B. Hassibi, Necessary and sufficient condtions for success of the nuclear norm heuristic for rank minimization, in 47th IEEE Conference on Decision and Control, (2008), 3065-3070. Google Scholar

[16]

M.C. Yue and A. M. C. So, A perturbation inequality for concave functions of singular values and its applications in low-rank matrix recovery, Applied and Computational Harmonic Analysis, 40 (2016), 396-416.  doi: 10.1016/j.acha.2015.06.006.  Google Scholar

[17]

M. Zhang, Z. H. Huang and Y. Zhang, Restricted $p$-isometry properties of nonconvex matrix recovery, IEEE Transactions on Information Theory, 59 (2013), 4316-4323.  doi: 10.1109/TIT.2013.2250577.  Google Scholar

[18]

Y. Q. Zhao and J. Yang, Hyperspectral image denoising via sparse representation and low-rank constraint, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 296-308.   Google Scholar

[19]

Z. Zheng, M. Yu, J. Jia, H. Liu, D. Xiang, X. Huang and J. Yang, Fisher discrimination based low rank matrix recovery for face recognition, Pattern Recognition, 47 (2014), 3502-3511.  doi: 10.1016/j.patcog.2014.05.001.  Google Scholar

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