標題: Bayesian Sparse Topic Model
作者: Chien, Jen-Tzung
Chang, Ying-Lan
電機資訊學士班
Undergraduate Honors Program of Electrical Engineering and Computer Science
關鍵字: Bayesian sparse learning;Feature selection;Topic model
公開日期: 1-三月-2014
摘要: This paper presents a new Bayesian sparse learning approach to select salient lexical features for sparse topic modeling. The Bayesian learning based on latent Dirichlet allocation (LDA) is performed by incorporating the spike-and-slab priors. According to this sparse LDA (sLDA), the spike distribution is used to select salient words while the slab distribution is applied to establish the latent topic model based on those selected relevant words. The variational inference procedure is developed to estimate prior parameters for sLDA. In the experiments on document modeling using LDA and sLDA, we find that the proposed sLDA does not only reduce the model perplexity but also reduce the memory and computation costs. Bayesian feature selection method does effectively identify relevant topic words for building sparse topic model.
URI: http://dx.doi.org/10.1007/s11265-013-0759-x
http://hdl.handle.net/11536/24002
ISSN: 1939-8018
DOI: 10.1007/s11265-013-0759-x
期刊: JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
Volume: 74
Issue: 3
起始頁: 375
結束頁: 389
顯示於類別:期刊論文


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