subspace clustering with sparsity and grouping effect

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ID: 253863
2017
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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.
Reference Key
zhang2017mathematicalsubspace Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Binbin Zhang;Weiwei Wang;Xiangchu Feng
Journal journal of power sources
Year 2017
DOI
10.1155/2017/4787039
URL
http://dx.doi.org/10.1155/2017/4787039
Keywords
civil engineering (general)mathematics engineering (general)

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