Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery

• URL: http://arxiv.org/abs/2501.00677v1
• Date: Tue, 31 Dec 2024 23:22:12 GMT
• Title: Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
• Authors: HanQin Cai, Chandra Kundu, Jialin Liu, Wotao Yin,
• Abstract summary: Robust feed cloud completion (RMC) is a widely used machine learning tool.<n>It simultaneously tackles two critical issues in low-rank data analysis: missing data and extreme outliers.<n>This paper proposes Robust Matrix Learned (LRMC) for largescale RMC problems.<n>The superior empirical performance of LRMC is verified with experiments against state-of-the-art on synthetic datasets and real applications.
• Score: 25.33005185616769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme outliers. This paper proposes a novel scalable and learnable non-convex approach, coined Learned Robust Matrix Completion (LRMC), for large-scale RMC problems. LRMC enjoys low computational complexity with linear convergence. Motivated by the proposed theorem, the free parameters of LRMC can be effectively learned via deep unfolding to achieve optimum performance. Furthermore, this paper proposes a flexible feedforward-recurrent-mixed neural network framework that extends deep unfolding from fix-number iterations to infinite iterations. The superior empirical performance of LRMC is verified with extensive experiments against state-of-the-art on synthetic datasets and real applications, including video background subtraction, ultrasound imaging, face modeling, and cloud removal from satellite imagery.

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