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A Gaussian-based rank approximation for subspace clustering
Author(s): Fei Xu; Chong Peng; Yunhong Hu; Guoping He
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Paper Abstract

Low-rank representation (LRR) has been shown successful in seeking low-rank structures of data relationships in a union of subspaces. Generally, LRR and LRR-based variants need to solve the nuclear norm-based minimization problems. Beyond the success of such methods, it has been widely noted that the nuclear norm may not be a good rank approximation because it simply adds all singular values of a matrix together and thus large singular values may dominant the weight. This results in far from satisfactory rank approximation and may degrade the performance of lowrank models based on the nuclear norm. In this paper, we propose a novel nonconvex rank approximation based on the Gaussian distribution function, which has demanding properties to be a better rank approximation than the nuclear norm. Then a low-rank model is proposed based on the new rank approximation with application to motion segmentation. Experimental results have shown significant improvements and verified the effectiveness of our method.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152B (10 April 2018); doi: 10.1117/12.2302503
Show Author Affiliations
Fei Xu, Shandong Univ. of Science and Technology (China)
Qingdao Univ. of Science and Technology (China)
Chong Peng, Southern Illinois Univ. Carbondale (United States)
Yunhong Hu, Yuncheng Univ. (China)
Guoping He, Shandong Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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