Max-Margin Nonparametric Latent Feature Models for Link Prediction

Abstract

We present a max-margin nonparametric latent feature model, which unites the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction and automatically infer the unknown latent social dimension. By minimizing a hinge-loss using the linear expectation operator, we can perform posterior inference efficiently without dealing with a highly nonlinear link likelihood function; by using a fully-Bayesian formulation, we can avoid tuning regularization constants. Experimental results on real datasets appear to demonstrate the benefits inherited from max-margin learning and fully-Bayesian nonparametric inference.

ICML2012

References in corpus (4)

• Modeling homophily and stochastic equivalence in symmetric relational data
• Max-Margin Nonparametric Latent Feature Models for Link Prediction
• Gibbs Max-margin Topic Models with Data Augmentation
• Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

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