Presentation 2002/6/20
Nonlinear Principal Component Analysis to Preserve the Order of Principal Components
Ryo Saegusa, Shuji Hashimoto,
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Abstract(in English) Principal component analysis is an effective method of liner dimensional compression for signal processing. However, when data have nonlinear structure, the result of the method has redundancy. To overcome this problem, numbers of non-linear principal component analyses were proposed. In these methods, we must decide the number of principal component in advance. We propose a hierarchical model of multi-layer perceptron to perform a new type of nonlinear principal component analysis, which preserves the order of principal components. We also demonstrate the effectiveness of proposed method through the experiments.
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Keyword(in English) Nonlinear Principal Component Analysis / Neural Network / Hierarchical Structure / Module
Paper # NC2002-14
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Committee NC
Conference Date 2002/6/20(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) Nonlinear Principal Component Analysis to Preserve the Order of Principal Components
Sub Title (in English)
Keyword(1) Nonlinear Principal Component Analysis
Keyword(2) Neural Network
Keyword(3) Hierarchical Structure
Keyword(4) Module
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 Department of Applied Physics, Faculty of Science and Engineering, Waseda University
Date 2002/6/20
Paper # NC2002-14
Volume (vol) vol.102
Number (no) 157
Page pp.pp.-
#Pages 6
Date of Issue