@inproceedings{35c40e83333b44829a861f97e6ae7a36,
title = "An algorithm of schatten p-norm regularized least squares problems for video restoration",
abstract = "Minimizing the nuclear norm is recently considered as the convex relaxation of the rank minimization problem and arises in many applications as Netflix challenge. A closest nonconvex relaxation-Schatten p(0 < p <1) norm minimization has been proposed to replace the NP hard rank minimization. In this paper, an algorithm based on Majorization Minimization has be proposed to solve Schatten p(0 < p <1) norm minimization. The numerical experiments show that Schatten p norm with 0 < p <1 recovers low rank matrix from fewer measurements than nuclear norm minimization. The numerical results also indicate that our algorithm give a more accurate reconstruction.",
keywords = "Isometry constant, Majorization minimization, Random matrix, Rank minimization, Schatten p-norm, Singular value decomposition",
author = "Lu Liu and Wei Huang and Chen, \{Di Rong\}",
year = "2013",
doi = "10.4028/www.scientific.net/AMR.718-720.2308",
language = "英语",
isbn = "9783037857168",
series = "Advanced Materials Research",
pages = "2308--2313",
booktitle = "Advanced Measurement and Test III",
note = "2013 3rd International Conference on Advanced Measurement and Test, AMT 2013 ; Conference date: 13-03-2013 Through 14-03-2013",
}