基于最小二乘回归的分块加权子空间聚类<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201612007

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Abstract Traditional subspace clustering algorithms need to transform each sample into a vector form. Therefore, problems of high dimensionality and small size samples are caused, the natural structural information of each sample is ignored and the clustering information is missing. To overcome the drawbacks, the weighted block subspace clustering based on least square regression algorithm (WB-LSR) is proposed. Firstly, each sample is divided into lots of blocks, and the corresponding affinity matrices of each block are obtained. Next, the weight of each affinity matrix is determined by mutual vote between affinity matrices. Finally, the weighted sum of affinity matrices is regarded as final affinity matrix. The experimental results on image datasets and motion segmentation video datasets show that the proposed method effectively improves clustering accuracy.

Key words： Subspace Clustering Structural Information Block Affinity Matrix Weight

Received: 11 March 2016

ZTFLH:	TP 311
	TP 371

Fund:Supported by National Natural Science Foundation of China (No.11571074,71273053), Natural Science Foundation of Fujian Province (No.2014J01009)

About author:: (LI Hui, born in 1989, master student. His research interests include data mining and pattern recognition.)
(CHEN Xiaoyun(Corresponding author), born in 1970, Ph.D., professor. Her research interests include data mining and pattern recognition.)

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LI Hui,CHEN Xiaoyun. Weighted Block Subspace Clustering Based on Least Square Regression[J]. , 2016, 29(12): 1114-1121.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201612007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I12/1114

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