Subspace Clustering with Sparsity and Grouping Effect

Binbin Zhang, Weiwei Wang and Xiangchu Feng

Mathematical Problems in Engineering, 2017, vol. 2017, 1-9

Abstract:

Subspace clustering aims to group a set of data from a union of subspaces into the subspace from which it was drawn. It has become a popular method for recovering the low-dimensional structure underlying high-dimensional dataset. The state-of-the-art methods construct an affinity matrix based on the self-representation of the dataset and then use a spectral clustering method to obtain the final clustering result. These methods show that sparsity and grouping effect of the affinity matrix are important in recovering the low-dimensional structure. In this work, we propose a weighted sparse penalty and a weighted grouping effect penalty in modeling the self-representation of data points. The experimental results on Extended Yale B, USPS, and Berkeley 500 image segmentation datasets show that the proposed model is more effective than state-of-the-art methods in revealing the subspace structure underlying high-dimensional dataset.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4787039

DOI: 10.1155/2017/4787039

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