Robust Subspace Recovery via Bi-Sparsity Pursuit

Xiao Bian; Hamid Krim

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arXiv · 1403.8067

Robust Subspace Recovery via Bi-Sparsity Pursuit

Xiao Bian·Hamid Krim

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Abstract

Successful applications of sparse models in computer vision and machine learning imply that in many real-world applications, high dimensional data is distributed in a union of low dimensional subspaces. Nevertheless, the underlying structure may be affected by sparse errors and/or outliers. In this paper, we propose a bi-sparse model as a framework to analyze this problem and provide a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We further show the effectiveness of our method by experiments on both synthetic data and real-world vision data.

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Xiao Bian, Hamid Krim. 2014-04-20. Robust Subspace Recovery via Bi-Sparsity Pursuit. https://arxiv.org/abs/1403.8067

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