Presentation 2015-11-01
The hierarchical visualizing and learning method in the generative topographic mapping
Takehito Oshita, Mikio Hasegawa,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Generative Topographic Mapping(GTM) is the latent variable model which is formalized Kohonen’s Self-Organization Map in the field of the probability theory. In this paper, We introduce the basic theory of GTM and the hierarchical visualization of MNIST(which is handwritten 10 numbers 0-9) dataset, and discuss the efficiency for the learning accuracy.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) GTM / Generative Model / Latent Variable / RBF Network / Visualization / Manifold Learning / SOM / Self Organization Mapping
Paper # NLP2015-124
Date of Issue 2015-10-24 (NLP)

Conference Information
Committee NLP
Conference Date 2015/10/31(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Nobumoto Ohama Memorial Hall
Topics (in Japanese) (See Japanese page)
Topics (in English) Nonlinear Problems, etc.
Chair Kenya Jinno(Nippon Inst. of Tech.)
Vice Chair Naoto Fujisaka(Hiroshima City Univ.)
Secretary Naoto Fujisaka(Tokyo Univ. of Science)
Assistant Hidehiro Nakano(Tokyo City Univ.) / Hiroyuki Asahara(Okayama Univ. of Science)

Paper Information
Registration To Technical Committee on Nonlinear Problems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) The hierarchical visualizing and learning method in the generative topographic mapping
Sub Title (in English)
Keyword(1) GTM
Keyword(2) Generative Model
Keyword(3) Latent Variable
Keyword(4) RBF Network
Keyword(5) Visualization
Keyword(6) Manifold Learning
Keyword(7) SOM
Keyword(8) Self Organization Mapping
1st Author's Name Takehito Oshita
1st Author's Affiliation Brains Consulting, Inc.(BCI)
2nd Author's Name Mikio Hasegawa
2nd Author's Affiliation Tokyo University of Science(Tokyo Univ. of Science)
Date 2015-11-01
Paper # NLP2015-124
Volume (vol) vol.115
Number (no) NLP-284
Page pp.pp.99-103(NLP),
#Pages 5
Date of Issue 2015-10-24 (NLP)