2017 13th International Conference on Computational Intelligence and Security (CIS)
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Abstract

We propose a method based on the collaborative Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) to cluster data drawn from multiple linear subspaces in a high-dimensional space. Given a set of data vectors, Collaborative Low-Rank and Sparse Subspace Clustering(CLRS) want to seek a better representation among the candidates that represent all vectors as affine combination of the bases in a dictionary. Both theoretical and experimental results show that CLRS is a promising method for subspace segmentation.
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