Dr. Chong Peng Profile

Dr. Chong Peng

at Southern Illinois Univ Carbondale

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Proceedings Article | 10 April 2018 Paper

A Gaussian-based rank approximation for subspace clustering

Fei Xu, Chong Peng, Yunhong Hu, Guoping He

Proceedings Volume 10615, 106152B (2018) https://doi.org/10.1117/12.2302503

KEYWORDS: Data modeling, Motion models, Image segmentation, Computer science, Machine vision, Control systems, Video, Computer engineering, Mathematics, Physics

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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.

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