Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions

• URL: http://arxiv.org/abs/2407.19446v1
• Date: Sun, 28 Jul 2024 09:47:36 GMT
• Title: Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions
• Authors: Tianming Wang, Ke Wei,
• Abstract summary: We rank the problem of robust completion matrix (RMC) We consider a simple yet efficient algorithm for the analysis. To the best sampling result, this is the first rank-out analysis method.
• Score: 7.697455644733554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of robust matrix completion (RMC), where the partially observed entries of an underlying low-rank matrix is corrupted by sparse noise. Existing analysis of the non-convex methods for this problem either requires the explicit but empirically redundant regularization in the algorithm or requires sample splitting in the analysis. In this paper, we consider a simple yet efficient nonconvex method which alternates between a projected gradient step for the low-rank part and a thresholding step for the sparse noise part. Inspired by leave-one out analysis for low rank matrix completion, it is established that the method can achieve linear convergence for a general class of thresholding functions, including for example soft-thresholding and SCAD. To the best of our knowledge, this is the first leave-one-out analysis on a nonconvex method for RMC. Additionally, when applying our result to low rank matrix completion, it improves the sampling complexity of existing result for the singular value projection method.

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