Enhanced Low-Rank Matrix Approximation

Ankit Parekh; Ivan W. Selesnick

doi:10.1109/lsp.2016.2535227

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arXiv · 1511.01966

Enhanced Low-Rank Matrix Approximation

Ankit Parekh·Ivan W. Selesnick

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Abstract

This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization. We employ parameterized non-convex penalty functions to estimate the non-zero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the non-convex regularizer) is also derived. The solution reduces to singular value thresholding method as a special case. The proposed method is demonstrated for image denoising.

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BibTeX RIS

Ankit Parekh, Ivan W. Selesnick. 2016-04-12. Enhanced Low-Rank Matrix Approximation. https://doi.org/10.1109/lsp.2016.2535227

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