一种基于低秩表示的子空间聚类改进算法

doi:10.11999/JEIT160009

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Abstract

The nuclear norm is used to replace the rank function in the subspace clustering algorithm based on low rank representation, it can not estimate the rank of the matrix effectively and it is sensitive to Gauss noise. In this paper, a novel algorithm is proposed to improve the accuracy and maintain its stability under the Gauss noise. When building the objective function, the nuclear norm and Forbenius norm of coefficient matrix are used as the regularization terms, after a strong convex regularizer over singular values of coefficient matrix, an inexact augmented Lagrange multiplier method is utilized to solve the problem. Finally, the affinity matrix is acquired by post-processing of coefficient matrix and the classical spectral clustering method is employed to clustering. The experimental comparison results between the state-of-the-art algorithms on synthetic data, Extended Yale B and PIE datasets demonstrate the effectiveness of the proposed improved method and the robustness to Gauss noise.

Key words： Subspace clustering Low rank representation Rank function Forbenius norm Augmented Lagrange multiplier method

Received: 04 January 2016 Published: 19 July 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61473154)

Corresponding Authors: ZHANG Tao E-mail: njustztwork@126.com

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	ZHANG Tao
	TANG Zhenmin
	Lü Jianyong

Cite this article:

ZHANG Tao,TANG Zhenmin,Lü Jianyong. Improved Algorithm Based on Low Rank Representation for Subspace Clustering[J]. JEIT, 2016, 38(11): 2811-2818.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160009 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2811

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