Factorized Asymptotic Bayesian Inference for Latent Feature Models
Kohei Hayashi, Ryohei Fujimaki · Neural Information Processing Systems · 2013
This paper extends asymptotic Bayesian (FAB) inference for latent feature models (LFMs). FAB inference has not been applicable to models, including LFMs, without a specific condition on the Hessian matrix of a complete log-likelihood, which is required to derive a factorized information criterion (FIC). Our asymptotic analysis of the Hessian matrix of LFMs shows that FIC of LFMs has the same form as those of mixture models. FAB/LFMs have several desirable properties (e.g., automatic hidden states selection and parameter identifiability) and empirically perform better than state-of-the-art Indian Buffet processes in terms of model selection, prediction, and computational efficiency.