Convex Collective Matrix Factorization
Abstract
In many applications, multiple interlinked sources of data are available and they cannot be represented by a single adjacency matrix, to which large scale factorization method could be applied. Collective matrix factorization is a simple yet powerful approach to jointly factorize multiple matrices, each of which represents a relation between two entity types. Existing algorithms to estimate parameters of collective matrix factorization models are based on non-convex formulations of the problem; in this paper, a convex formulation of this approach is proposed. This enables the derivation of large scale algorithms to estimate the parameters, including an iterative eigenvalue thresholding algorithm. Numerical experiments illustrate the benefits of this new approach.
Cite
Text
Bouchard et al. "Convex Collective Matrix Factorization." International Conference on Artificial Intelligence and Statistics, 2013.Markdown
[Bouchard et al. "Convex Collective Matrix Factorization." International Conference on Artificial Intelligence and Statistics, 2013.](https://mlanthology.org/aistats/2013/bouchard2013aistats-convex/)BibTeX
@inproceedings{bouchard2013aistats-convex,
title = {{Convex Collective Matrix Factorization}},
author = {Bouchard, Guillaume and Yin, Dawei and Guo, Shengbo},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2013},
pages = {144-152},
url = {https://mlanthology.org/aistats/2013/bouchard2013aistats-convex/}
}