Name
Abstract | ||
|---|---|---|
This paper extends factorized 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 Hesqsian 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." |
Year | Venue | Field |
|---|---|---|
2013 | NIPS | Bayesian inference,Computer science,Identifiability,Hessian matrix,Artificial intelligence,Asymptotic analysis,Mathematical optimization,Pattern recognition,Inference,Model selection,Algorithm,Mixture model,Bayesian probability |
DocType | Citations | PageRank |
Conference | 8 | 0.57 |
References | Authors | |
18 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hayashi, Kohei | 1 | 159 | 15.31 |
| Ryohei Fujimaki | 2 | 193 | 16.93 |