Deep Unrolling for Nonconvex Robust Principal Component Analysis
Elizabeth Z. C. Tan,
Caroline Chaux,
Emmanuel Soubies
et al.
Abstract:We design algorithms for Robust Principal Component Analysis (RPCA) which consists in decomposing a matrix into the sum of a low rank matrix and a sparse matrix. We propose a deep unrolled algorithm based on an accelerated alternating projection algorithm which aims to solve RPCA in its nonconvex form. The proposed procedure combines benefits of deep neural networks and the interpretability of the original algorithm and it automatically learns hyperparameters. We demonstrate the unrolled algorithm's effectiven… Show more
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