Fast Max-Margin Matrix Factorization with Data Augmentation

Minjie Xu, Jun Zhu, Bo Zhang
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):978-986, 2013.

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 this Paper

BibTeX
@InProceedings{pmlr-v28-xu13a, title = {Fast Max-Margin Matrix Factorization with Data Augmentation}, author = {Xu, Minjie and Zhu, Jun and Zhang, Bo}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {978--986}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/xu13a.pdf}, url = {https://proceedings.mlr.press/v28/xu13a.html}, 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.} }
Endnote
%0 Conference Paper %T Fast Max-Margin Matrix Factorization with Data Augmentation %A Minjie Xu %A Jun Zhu %A Bo Zhang %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-xu13a %I PMLR %P 978--986 %U https://proceedings.mlr.press/v28/xu13a.html %V 28 %N 3 %X 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.
RIS
TY - CPAPER TI - Fast Max-Margin Matrix Factorization with Data Augmentation AU - Minjie Xu AU - Jun Zhu AU - Bo Zhang BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-xu13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 978 EP - 986 L1 - http://proceedings.mlr.press/v28/xu13a.pdf UR - https://proceedings.mlr.press/v28/xu13a.html AB - 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. ER -
APA
Xu, M., Zhu, J. & Zhang, B.. (2013). Fast Max-Margin Matrix Factorization with Data Augmentation. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):978-986 Available from https://proceedings.mlr.press/v28/xu13a.html.

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