2016年
A simple stochastic gradient variational bayes for the correlated topic model
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- ,
- 巻
- 9932
- 号
- 開始ページ
- 424
- 終了ページ
- 428
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-319-45817-5_39
- 出版者・発行元
- Springer Verlag
This paper proposes a new inference for the correlated topic model (CTM) [3]. CTM is an extension of LDA [4] for modeling correlations among latent topics. The proposed inference is an instance of the stochastic gradient variational Bayes (SGVB) [7,8]. By constructing the inference network with the diagonal logistic normal distribution, we achieve a simple inference. Especially, there is no need to invert the covariance matrix explicitly. We performed a comparison with LDA in terms of predictive perplexity. The two inferences for LDA are considered: the collapsed Gibbs sampling (CGS) [5] and the collapsed variational Bayes with a zero-order Taylor expansion approximation (CVB0) [1]. While CVB0 for LDA gave the best result, the proposed inference achieved the perplexities comparable with those of CGS for LDA.
- リンク情報
- ID情報
-
- DOI : 10.1007/978-3-319-45817-5_39
- ISSN : 1611-3349
- ISSN : 0302-9743
- DBLP ID : conf/apweb/MasadaT16
- SCOPUS ID : 84990055146