Fast Max-Margin Matrix Factorization with Data Augmentation
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Abstract
Existing max-margin matrix factorization (
) 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 
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 
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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