  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.
