Matrix Approximation Under Local Low-Rank Assumption
Abstract
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.
Cite
Text
Lee et al. "Matrix Approximation Under Local Low-Rank Assumption." International Conference on Learning Representations, 2013.Markdown
[Lee et al. "Matrix Approximation Under Local Low-Rank Assumption." International Conference on Learning Representations, 2013.](https://mlanthology.org/iclr/2013/lee2013iclr-matrix/)BibTeX
@inproceedings{lee2013iclr-matrix,
title = {{Matrix Approximation Under Local Low-Rank Assumption}},
author = {Lee, Joonseok and Kim, Seungyeon and Lebanon, Guy and Singer, Yoram},
booktitle = {International Conference on Learning Representations},
year = {2013},
url = {https://mlanthology.org/iclr/2013/lee2013iclr-matrix/}
}