Dirichlet mixture allocation

Jen-Tzung Chien, Chao Hsi Lee, Zheng Hua Tan

研究成果: Conference contribution同行評審

摘要

The topic model based on latent Dirichlet allocation relies on the prior statistics of topic proportionals for multinomial words. The words in a document are modeled as a random mixture of latent topics which are drawn from a single Dirichlet prior. However, a single Dirichlet distribution may not sufficiently characterize the variations of topic proportionals estimated from the heterogeneous documents. To deal with this concern, we present a Dirichlet mixture allocation (DMA) model which learns latent topics and their proportionals for topic and document clustering by using the prior based on a Dirichlet mixture model. Multiple Dirichlets pave a way to capture the structure of latent variables in learning representation from real-world documents covering a variety of topics. This paper builds a new latent variable model and develops a variational Bayesian inference procedure to learn model parameters consisting of mixture weights, Dirichlet parameters and word multinomials. Experiments on document representation show the merit of the proposed structural learning by increasing the number of Dirichlets in a DMA topic model.

原文English
主出版物標題2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
編輯Kostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
發行者IEEE Computer Society
ISBN(電子)9781509007462
DOIs
出版狀態Published - 8 十一月 2016
事件26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
持續時間: 13 九月 201616 九月 2016

出版系列

名字IEEE International Workshop on Machine Learning for Signal Processing, MLSP
2016-November
ISSN(列印)2161-0363
ISSN(電子)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
國家/地區Italy
城市Vietri sul Mare, Salerno
期間13/09/1616/09/16

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