Deep Unrolling for Nonconvex Robust Principal Component Analysis

• URL: http://arxiv.org/abs/2307.05893v1
• Date: Wed, 12 Jul 2023 03:48:26 GMT
• Title: Deep Unrolling for Nonconvex Robust Principal Component Analysis
• Authors: Elizabeth Z. C. Tan, Caroline Chaux, Emmanuel Soubies, Vincent Y. F. Tan
• Abstract summary: We design algorithms for Robust Component Analysis (A) It consists in decomposing a matrix into the sum of a low Principaled matrix and a sparse Principaled matrix.
• Score: 75.32013242448151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 effectiveness on synthetic datasets and also on a face modeling problem, where it leads to both better numerical and visual performances.

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