基于协同表示的子空间聚类<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201703007

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Abstract The coefficient matrix solved by sparse subspace clustering(SSC) is too sparse and the coefficient matrix solved by least squares regression for subspace clustering(LSR) is too dense. Aiming at these problems, subspace clustering based on collaborative representation(SCCR) is proposed. The advantages of SSC and LSR are combined. The l₁ norm and the Frobenius norm are introduced into an objective function. The coefficient solved by SCCR can group the correlated data within cluster like LSR and eliminate the connection between clusters like SSC. Then, the affinity matrix constructed by this coefficient matrix is applied to spectral clustering. The experimental results demonstrate that SCCR improves the performance of clustering.

Key words： Sparse Subspace Clustering Least Squares Regression for Subspace Clustering Frobenius Norm Spectral Clustering

Received: 05 July 2016

ZTFLH:

TP 301.6

Fund:Supported by National Natural Science Foundation of China(No.61672265,61373055), Industrialization Project of Jiangsu Educational Department(No.JH10-28), Production and Research Innovation Project of Jiangsu Province(No.BY2012059)

About author:: FU Wenjin, born in 1992, master student. His research interests include cluster analysis.
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 recognition.
YIN Hefeng, born in 1989, Ph.D. candidate. His research interests include sparse representation based classification and low rank matrix recovery.

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Cite this article:

FU Wenjin,WU Xiaojun,DONG Wenhua等. Subspace Clustering Based on Collaborative Representation[J]. , 2017, 30(3): 251-259.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201703007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I3/251

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