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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Nonlinear Principal Component Analysis / Neural Network / Hierarchical Structure / Module |
Paper # | NC2002-14 |
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Committee | NC |
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Conference Date | 2002/6/20(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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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 |
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