Robust Subspace Recovery via Dual Sparsity Pursuit

The implementation for this solver is not available, here is another phase diagram for the Robust PCA decomposition. 

the previous one is featured in the Advanced Matrix Factorization Jungle page from Bilinear Generalized Approximate Message Passing by Jason T. Parker, Philip Schniter, Volkan Cevher. One wonders how the two diagrams fit with each other. Here is the paper: Robust Subspace Recovery via Dual Sparsity Pursuit by Xiao Bian, Hamid Krim

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 dual 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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