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Fast Max-Margin Matrix Factorization with Data Augmentation
Authors: Minjie Xu, Jun Zhu and Bo Zhang
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
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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