| Presentation | 2012-01-27 Adaptive Algorithms for Kernel Principal Components Tracking Toshihisa TANAKA, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Adaptive online algorithms for simultaneously extracting nonlinear eigenvectors of kernel principal component analysis (KPCA) are developed. KPCA needs all the observed samples to represent basis functions, and the same scale of eigenvalue problem as the number of samples should be solved. This paper reformulates KPCA and deduces an expression in the Euclidean space, where an algorithm for tracking generalized eigenvectors is applicable. The developed algorithm is least mean squares (LMS)-type and recursive least squares (RLS)-type. Numerical example is then illustrated to support the analysis. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | SIP2011-101,RCS2011-290 |
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| Conference Information | |
| Committee | RCS |
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| Conference Date | 2012/1/19(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Radio Communication Systems (RCS) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Adaptive Algorithms for Kernel Principal Components Tracking |
| Sub Title (in English) | |
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| 1st Author's Name | Toshihisa TANAKA |
| 1st Author's Affiliation | Department of Electrical and Electronic Engineering Tokyo University of Agriculture and Technology() |
| Date | 2012-01-27 |
| Paper # | SIP2011-101,RCS2011-290 |
| Volume (vol) | vol.111 |
| Number (no) | 404 |
| Page | pp.pp.- |
| #Pages | 5 |
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