| Presentation | 2012-07-02 A method for tracking kernel principal subspace Toshihisa TANAKA, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In the kernel principal component analysis (KPCA), since all the nonlinear functions associated with observed samples are the basis functions for eigenvectors, it is necessary to solve the same scale of eigenvalue problem as the number of samples should be solved. This paper finds the eigenvectors on a subspace in the Euclidean space. This subspace adaptively changes depending on input signals, and we develop recursive least squares (RLS)-type algorithm for tracking a kernel principal subspace on this adaptively changing subspace. Numerical example is then illustrated to support the analysis. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | CAS2012-6,VLD2012-16,SIP2012-38,MSS2012-6 |
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| Conference Information | |
| Committee | MSS |
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| Conference Date | 2012/6/25(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Mathematical Systems Science and its applications(MSS) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A method for tracking kernel principal subspace |
| 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 | 2012-07-02 |
| Paper # | CAS2012-6,VLD2012-16,SIP2012-38,MSS2012-6 |
| Volume (vol) | vol.112 |
| Number (no) | 116 |
| Page | pp.pp.- |
| #Pages | 6 |
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