Presentation	1998/3/19 Self-Organizing Extraction of Principal Components by Continuously Multiple Mapping Jun SHIRAKURA, Koji KURATA,
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Abstract(in English)	Layered self-organizing map model is proposed to detect the first and the second principal components. The model is an advanced version of the self-organizing multiple mapping model proposed and studied in our preceding papers. This model inherits the nature from our multiple mapping model and the resulting map for the second principal component is understood as superposition of many correlating maps. This model can be applied to information sources of high-dimensional and low-dimensional representation. In high-dimensional case, it can detect nonlinear components.
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Keyword(in English)	self-organization / topological mapping / continuously multiple mapping / NLPCA
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Committee	NC
Conference Date	1998/3/19(1days)
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Registration To	Neurocomputing (NC)
Language	JPN
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Title (in English)	Self-Organizing Extraction of Principal Components by Continuously Multiple Mapping
Sub Title (in English)
Keyword(1)	self-organization
Keyword(2)	topological mapping
Keyword(3)	continuously multiple mapping
Keyword(4)	NLPCA
1st Author's Name	Jun SHIRAKURA
1st Author's Affiliation	Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University.()
2nd Author's Name	Koji KURATA
2nd Author's Affiliation	Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University.
Date	1998/3/19
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Volume (vol)	vol.97
Number (no)	623
Page	pp.pp.-
#Pages	8
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