# Matrix Approximation under Local Low-Rank Assumption

arXiv: https://arxiv.org/abs/1301.3192 · HTML: https://mlsanity.com/paper/1301.3192/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2013.

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

- [Initialization for Nonnegative Matrix Factorization: a Comprehensive Review](https://mlsanity.com/paper/2109.03874/) (2021)

## Abstract (arXiv)

Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy in recommendation tasks.