Presentation	2003/9/1 Nonlinear Principal Component Analysis on Real-World Data Ryo SAEGUSA, Shuji HASHIMOTO,
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Abstract(in English)	Principal Component Analysis (PCA) is an useful method in multivariate analysis to reduce a dimensionality of data. We have already proposed a nonlinearly extended model of PCA and have shown its effectiveness with some artificial data. In this paper, we report results of a nonlinear principal component analysis on real-world data utilizing the proposed method. Moreover, we compare the distribution of transformed data by nonlinear mappings with the distribution of the original data to discuss a nonlinearity of the data distribution.
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Keyword(in English)	nonlinear principal component analysis / hierarchical structure / multi-layered perceptron
Paper #	PRMU2003-84
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Conference Information
Committee	PRMU
Conference Date	2003/9/1(1days)
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Paper Information
Registration To	Pattern Recognition and Media Understanding (PRMU)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Nonlinear Principal Component Analysis on Real-World Data
Sub Title (in English)
Keyword(1)	nonlinear principal component analysis
Keyword(2)	hierarchical structure
Keyword(3)	multi-layered perceptron
1st Author's Name	Ryo SAEGUSA
1st Author's Affiliation	Graduate School of Science and Engineering, Waseda University()
2nd Author's Name	Shuji HASHIMOTO
2nd Author's Affiliation	School of Science and Engineering, Waseda University
Date	2003/9/1
Paper #	PRMU2003-84
Volume (vol)	vol.103
Number (no)	295
Page	pp.pp.-
#Pages	6
Date of Issue