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The Infinite Relational Model (IRM) is a probabilistic model for relationaldata clustering that partitions objects into clusters based on observedrelationships. This paper presents Averaged CVB (ACVB) solutions for IRM,convergence-guaranteed and practically useful fast Collapsed Variational Bayes(CVB) inferences. We first derive ordinary CVB and CVB0 for IRM based on thelower bound maximization. CVB solutions yield deterministic iterativeprocedures for inferring IRM given the truncated number of clusters. Ourproposal includes CVB0 updates of hyperparameters including the concentrationparameter of the Dirichlet Process, which has not been studied in theliterature. To make the CVB more practically useful, we further study the CVBinference in two aspects. First, we study the convergence issues and develop aconvergence-guaranteed algorithm for any CVB-based inferences called ACVB,which enables automatic convergence detection and frees non-expertpractitioners from difficult and costly manual monitoring of inferenceprocesses. Second, we present a few techniques for speeding up IRM inferences.In particular, we describe the linear time inference of CVB0, allowing the IRMfor larger relational data uses. The ACVB solutions of IRM showed comparable orbetter performance compared to existing inference methods in experiments, andprovide deterministic, faster, and easier convergence detection.

Collapsed Variational Bayes Inference of Infinite Relational Model