Max-Margin Nonparametric Latent Feature Models for Link Prediction

2012/06/18 by Jun Zhu, Zhu, Jun · 3 citations
Computer Science · Mathematics · #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Topic Modeling #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1206.4659

ICML2012

arxiv created 2012/06/18 · openalex publication_date 2012/06/18 · arxiv updated 2012/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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