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Fast Max-Margin Matrix Factorization with Data Augmentation

Minjie Xu, Jun Zhu, Bo Zhang
;
JMLR W&CP 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.

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