Presentation 2012-11-07
Feature Selection for LDA using Bayesian Network
Ryosuke OHATA, Maomi UENO,
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Abstract(in English) Recently there has been great interest in topic model analyzing discrete data accompanied by arbitrary features, such as authors. However, previous work in such topic modeling does not take account of feature selection. This paper presents a feature selection method for such topic model. The proposed method selects the feature set involving data generation by clarifying causality between the latent topic and features using bayesian network-based approach. The Advantages of the proposed method are as follows: First, computational time is short because the proposed method can select the feature set without learning latent topic of any combination of features. Second, feature selection accuracy is high because the proposed method has consistency. We demonstrate the effectiveness of the proposed method by simulation.
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Keyword(in English) topic model / feature selection / bayesian network
Paper # IBISML2012-53
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Conference Information
Committee IBISML
Conference Date 2012/10/31(1days)
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Paper Information
Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Feature Selection for LDA using Bayesian Network
Sub Title (in English)
Keyword(1) topic model
Keyword(2) feature selection
Keyword(3) bayesian network
1st Author's Name Ryosuke OHATA
1st Author's Affiliation Graduate School of Information Systems, The University of Electro-Communications()
2nd Author's Name Maomi UENO
2nd Author's Affiliation Graduate School of Information Systems, The University of Electro-Communications
Date 2012-11-07
Paper # IBISML2012-53
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 8
Date of Issue