Presentation | 2002/7/19 On the Effects of applying Tsallis mutual entropy to Independent Component Analysis Hiroki SUYARI, Masumi DAKEMOTO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | When we apply independent component analysis (ICA for short) to extraction of some information sources from given observations, the results often depend on initial settings of the algorithm. In somes cases, we cannot find the required resuluts by any setting in ICA. In this paper, we apply Tsallis mutual entropy, one-parameter generalization of Shannon mutual entropy, to ICA and reports the effects of the new parameter (q ∈ [0,1]) in iCA. In particular, we take the examples which the usual ICA using Shannon mutual entropy cannot succeed to extract. It is shown that the application of Tsallis mutual entropy to ICA for such examples enables us to succeed in the extraction. The effect of the new parameter q in the learning is also discussed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | independent component analysis / Tsallis entropy / Tsallis mutual entropy |
Paper # | NC2002-34 |
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Committee | NC |
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Conference Date | 2002/7/19(1days) |
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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) | On the Effects of applying Tsallis mutual entropy to Independent Component Analysis |
Sub Title (in English) | |
Keyword(1) | independent component analysis |
Keyword(2) | Tsallis entropy |
Keyword(3) | Tsallis mutual entropy |
1st Author's Name | Hiroki SUYARI |
1st Author's Affiliation | Faculty of Engineering, Chiba University() |
2nd Author's Name | Masumi DAKEMOTO |
2nd Author's Affiliation | Graduate School of Science and Technology, Chiba University |
Date | 2002/7/19 |
Paper # | NC2002-34 |
Volume (vol) | vol.102 |
Number (no) | 253 |
Page | pp.pp.- |
#Pages | 5 |
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