Presentation 2013-11-22
Verification of Effectiveness of the Probabilistic Algorithm for Latent Structure Extraction Using Associative Memory Model
Kensuke WAKASUGI, Tatsu KUWATANI, Kenji NAGATA, Hideki ASOH, Masato OKADA,
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Abstract(in English) There are many analysis methods for high-dimensional data, but, in many cases, analysis is done under assumption that a latent structure in data is known. If there is analysis method that could estimate a kind of latent structure in data without that assumption, the analysis method becomes more general analysis method. In the previous study, the analysis method that select a latent structure in data from formal structures that has unknown structure was proposed. In this paper, we generate data from associative memory model which has an obvious structure, and we analyze the data with the proposed method. And then, we verify the validity of the analysis result.
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Keyword(in English) Machine learning / Associative memory model / Hierarchical structure / Bayesian estimate
Paper # NC2013-49
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Committee NC
Conference Date 2013/11/15(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Verification of Effectiveness of the Probabilistic Algorithm for Latent Structure Extraction Using Associative Memory Model
Sub Title (in English)
Keyword(1) Machine learning
Keyword(2) Associative memory model
Keyword(3) Hierarchical structure
Keyword(4) Bayesian estimate
1st Author's Name Kensuke WAKASUGI
1st Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo()
2nd Author's Name Tatsu KUWATANI
2nd Author's Affiliation Graduate School of Environmental Studies, Tohoku University
3rd Author's Name Kenji NAGATA
3rd Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo
4th Author's Name Hideki ASOH
4th Author's Affiliation The National Institute of Advanced Industrial Science and Technology
5th Author's Name Masato OKADA
5th Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute
Date 2013-11-22
Paper # NC2013-49
Volume (vol) vol.113
Number (no) 315
Page pp.pp.-
#Pages 6
Date of Issue