# On a Problem of Weighted Low-Rank Approximation of Matrices

arXiv: https://arxiv.org/abs/1511.00649 · HTML: https://mlsanity.com/paper/1511.00649/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss](https://mlsanity.com/paper/2303.03027/) (2023)
- [Understanding Learning Invariance in Deep Linear Networks](https://mlsanity.com/paper/2506.13714/) (2025)
- [Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning](https://mlsanity.com/paper/2402.06884/) (2024)

## Abstract (arXiv)

We study a weighted low rank approximation that is inspired by a problem of constrained low rank approximation of matrices as initiated by the work of Golub, Hoffman, and Stewart (Linear Algebra and Its Applications, 88-89(1987), 317-327). Our results reduce to that of Golub, Hoffman, and Stewart in the limiting cases. We also propose an algorithm based on the alternating direction method to solve our weighted low rank approximation problem and compare it with the state-of-art general algorithms such as the weighted total alternating least squares and the EM algorithm.