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arXiv · 1206.4659

Max-Margin Nonparametric Latent Feature Models for Link Prediction

Jun Zhu
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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.

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Jun Zhu. 2012-06-18. Max-Margin Nonparametric Latent Feature Models for Link Prediction. https://arxiv.org/abs/1206.4659

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