Matrix Completion with Convex Optimization and Column Subset Selection

• URL: http://arxiv.org/abs/2403.01919v3
• Date: Sun, 06 Oct 2024 08:21:40 GMT
• Title: Matrix Completion with Convex Optimization and Column Subset Selection
• Authors: Antonina Krajewska, Ewa Niewiadomska-Szynkiewicz,
• Abstract summary: We present two algorithms that implement our Columns Selected Matrix Completion (CSMC) method. To study the influence of the matrix size, rank computation, and the proportion of missing elements on the quality of the solution and the time, we performed experiments on synthetic data. Our thorough analysis shows that CSMC provides solutions of comparable quality to matrix completion algorithms, which are based on convex optimization.
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• Abstract: We introduce a two-step method for the matrix recovery problem. Our approach combines the theoretical foundations of the Column Subset Selection and Low-rank Matrix Completion problems. The proposed method, in each step, solves a convex optimization task. We present two algorithms that implement our Columns Selected Matrix Completion (CSMC) method, each dedicated to a different size problem. We performed a formal analysis of the presented method, in which we formulated the necessary assumptions and the probability of finding a correct solution. In the second part of the paper, we present the results of the experimental work. Numerical experiments verified the correctness and performance of the algorithms. To study the influence of the matrix size, rank, and the proportion of missing elements on the quality of the solution and the computation time, we performed experiments on synthetic data. The presented method was applied to two real-life problems problems: prediction of movie rates in a recommendation system and image inpainting. Our thorough analysis shows that CSMC provides solutions of comparable quality to matrix completion algorithms, which are based on convex optimization. However, CSMC offers notable savings in terms of runtime.

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