Max-Margin Nonparametric Latent Feature Models for Link Prediction
This work addresses link prediction in social networks, offering a method that combines discriminative and Bayesian techniques, though it appears incremental as it builds on existing paradigms.
The paper tackles link prediction by developing a max-margin nonparametric latent feature model that unites max-margin learning and Bayesian nonparametrics to discover discriminative latent features and infer unknown social dimensions, with experimental results on real datasets showing benefits from this approach.
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.