Presentation | 1998/6/18 Energy Functions for Efficient Nonlinear Dimensionality Reduction by Multi Layer Perceptrons Takashi TAKAHASHI, Ryuji TOKUNAGA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper investigates two simple energy functions which are valid for two different purposes of dimensionality reduction : feature extraction and data compression. These energy functions enable nonlinear perceptrons to organize data representations whose parameters, namely, outputs of the bottleneck layer units, are arranged in the order of their importance. The efficacy of these energy functions is shown by numerical experiments in comparison with conventional squared error functions and Principal Component Analysis. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | multi layer perceptron / feature extraction / data compression / principal component analysis |
Paper # | PRMU98-31 |
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Committee | PRMU |
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Conference Date | 1998/6/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Energy Functions for Efficient Nonlinear Dimensionality Reduction by Multi Layer Perceptrons |
Sub Title (in English) | |
Keyword(1) | multi layer perceptron |
Keyword(2) | feature extraction |
Keyword(3) | data compression |
Keyword(4) | principal component analysis |
1st Author's Name | Takashi TAKAHASHI |
1st Author's Affiliation | Doctoral Program in Engineering, University of Tsukuba() |
2nd Author's Name | Ryuji TOKUNAGA |
2nd Author's Affiliation | Institute of Information Sciences and Electronics, University of Tsukuba |
Date | 1998/6/18 |
Paper # | PRMU98-31 |
Volume (vol) | vol.98 |
Number (no) | 126 |
Page | pp.pp.- |
#Pages | 6 |
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