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Home > Archive>Volume 28, Issue 12, 2017 >3347-3357. DOI:10.13328/j.cnki.jos.005235
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Weighted Low Rank Subspace Clustering Based on A2 Norm
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National Natural Science Foundation of China (61373055, 61672265), Industry Project of Provincial Department of Education of Jiangsu Province (JH10-28)

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    Abstract:

    In order to solve the problem of over-sparsity for within-class coefficients and over-density for between-class coefficients in SSC and LSR, this paper proposes a new subspace clustering based on Euclidean distance using A2 norm. Using the weighted method based on Euclidean distance, the coefficient representation obtained by this algorithm maintains the connections of the data points from the same subspace. Meanwhile, the algorithm can eliminate the connections between clusters. The clusters can be produced by using the spectral clustering with the similarity matrix which is constructed by this coefficient representation. The results of experiments indicate the presented method improves the accuracy of clustering.

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傅文进,吴小俊.基于A2范数的加权低秩子空间聚类.软件学报,2017,28(12):3347-3357

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History
  • Received:February 28,2016
  • Revised:August 10,2016
  • Online: March 27,2017
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