Fast Max-Margin Matrix Factorization with Data Augmentation
Abstract
Existing max-margin matrix factorization (M3F) methods either are computationally inefficient or need a model selection procedure to determine the number of latent factors. In this paper we present a probabilistic M3F model that admits a highly efficient Gibbs sampling algorithm through data augmentation. We further extend our approach to incorporate Bayesian nonparametrics and build accordingly a truncation-free nonparametric M3F model where the number of latent factors is literally unbounded and inferred from data. Empirical studies on two large real-world data sets verify the efficacy of our proposed methods.
Cite
Text
Xu et al. "Fast Max-Margin Matrix Factorization with Data Augmentation." International Conference on Machine Learning, 2013.Markdown
[Xu et al. "Fast Max-Margin Matrix Factorization with Data Augmentation." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/xu2013icml-fast/)BibTeX
@inproceedings{xu2013icml-fast,
title = {{Fast Max-Margin Matrix Factorization with Data Augmentation}},
author = {Xu, Minjie and Zhu, Jun and Zhang, Bo},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {978-986},
volume = {28},
url = {https://mlanthology.org/icml/2013/xu2013icml-fast/}
}