Presentation | 1999/5/20 Supervised Independent Component Analysis and Its Applications to Face Image Analysis shinji Umeyama, Shotaro Akaho, Yasuko Sugase, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Independent Component analysis is a new data analysis method, and its computation algorithms and applications are widely studied recently. ICA decomposes observed data into several components bu using a criterion of statistical independence. However, observed data are sometimes obtained through some control signal, for ecample a visual stimulus in fMRI measurements. In this case it may be effective to use a control signal to salvage a independent component correlated woth it. We propose here such a method by extending ICA, and name it SICA (Supervised Independent Component Analysis). We also give several results of human face image analysis as an application of SICA. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | independent component analysis / Principal component analysis / supervised / face image |
Paper # | NC99-2 |
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Committee | NC |
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Conference Date | 1999/5/20(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) | Supervised Independent Component Analysis and Its Applications to Face Image Analysis |
Sub Title (in English) | |
Keyword(1) | independent component analysis |
Keyword(2) | Principal component analysis |
Keyword(3) | supervised |
Keyword(4) | face image |
1st Author's Name | shinji Umeyama |
1st Author's Affiliation | Electrotechnical Laboratory() |
2nd Author's Name | Shotaro Akaho |
2nd Author's Affiliation | Electrotechnical Laboratory |
3rd Author's Name | Yasuko Sugase |
3rd Author's Affiliation | National Institute of Bioscience and Human-Technology |
Date | 1999/5/20 |
Paper # | NC99-2 |
Volume (vol) | vol.99 |
Number (no) | 58 |
Page | pp.pp.- |
#Pages | 8 |
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