Presentation | 2004/6/17 Independent Component Analysis and Principal Component Analysis by a Coupled Pair of One-dimensional Self-Organizing Maps Takashi TAJIRI, Koji KURATA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Shirakura and Kurata proposed a model for information separation consisting of two self-organizing maps, which are one-dimensional each and connected by plastic inhibitory connections. With inputs parametrized by two parameters the model could form a map of each parameter on each one-dimensional self-organizing map. Slightly modified for better performance but essentially a same model was examined in detail to clarify what kind of information separation is achieved by the model. Two versions of the model was numerically studied and compared. Feedforward inhibition model was shown to organize maps of PCA-like representation, while recurrent inhibition model organized ICA-like representation. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Self-Organizing Maps / Nerve field / Independent Component Analysis / Principal Component Analysis |
Paper # | NC2004-22 |
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Committee | NC |
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Conference Date | 2004/6/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (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) | Independent Component Analysis and Principal Component Analysis by a Coupled Pair of One-dimensional Self-Organizing Maps |
Sub Title (in English) | |
Keyword(1) | Self-Organizing Maps |
Keyword(2) | Nerve field |
Keyword(3) | Independent Component Analysis |
Keyword(4) | Principal Component Analysis |
1st Author's Name | Takashi TAJIRI |
1st Author's Affiliation | Graduate school of Engineering and Science, University of the Ryukyus() |
2nd Author's Name | Koji KURATA |
2nd Author's Affiliation | Faculty of Engineering, University of the Ryukyus |
Date | 2004/6/17 |
Paper # | NC2004-22 |
Volume (vol) | vol.104 |
Number (no) | 139 |
Page | pp.pp.- |
#Pages | 6 |
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