Local Low-Rank Matrix Approximation
Abstract
Matrix approximation is a common tool in 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 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 over classical approaches for recommendation tasks.
Cite
Text
Lee et al. "Local Low-Rank Matrix Approximation." International Conference on Machine Learning, 2013.Markdown
[Lee et al. "Local Low-Rank Matrix Approximation." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/lee2013icml-local/)BibTeX
@inproceedings{lee2013icml-local,
title = {{Local Low-Rank Matrix Approximation}},
author = {Lee, Joonseok and Kim, Seungyeon and Lebanon, Guy and Singer, Yoram},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {82-90},
volume = {28},
url = {https://mlanthology.org/icml/2013/lee2013icml-local/}
}