# Enhanced Low-Rank Matrix Approximation

arXiv: https://arxiv.org/abs/1511.01966 · HTML: https://mlsanity.com/paper/1511.01966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion](https://mlsanity.com/paper/2310.04954/) (2023)

## Abstract (arXiv)

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.