局部约束加强的最小二乘回归子空间聚类<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201710009

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Abstract Least square regression subspace clustering(LSR) is the lack of local correlation information of data, and thus dense representation is caused. Aiming at this problem, locality constraint enhanced least squares regression subspace clustering(LC_LSR) is proposed. The original algorithm of LSR is extended by adding the local correlation constraint to achieve an accurate coefficient matrix and then it is close to being block diagonal. Furthermore, a method to construct affinity matrix is proposed. The proposed algorithm can better strengthen the affinities within each cluster and weaken the ones across clusters. Experimental results show that the proposed algorithm effectively improves the accuracy of clustering and its effectiveness and feasibility are verified.

Key words： Local Correlation Least Squares Regression Subspace Clustering Affinity Matrix

Received: 20 January 2017

ZTFLH:

TP 301.6

Fund:Supported by National Natural Science Foundation of China(No.61672265,61373055)

About author:: (ZHAO Jian, born in 1991, master student. His research interests include cluster analysis and pattern recognition.)
(WU Xiaojun(Corresponding author), born in 1967, Ph.D., professor. His research interests include artificial intelligence, pattern recognition and computer vision.)
(DONG Wenhua, born in 1975, Ph.D. candidate, lecturer. His research interests include artificial intelligence and pattern re-cognition.)

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	ZHAO Jian
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	DONG Wenhua

Cite this article:

ZHAO Jian,WU Xiaojun,DONG Wenhua. Locality Constraint Enhanced Least Squares Regression Subspace Clustering[J]. , 2017, 30(10): 943-951.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201710009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I10/943

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