Online feature extraction based on accelerated kernel principal component analysis for data stream
Kernel principal component analysis (KPCA) is known as a nonlinear feature extraction method. Takeuchi et al. have proposed an incremental type of KPCA (IKPCA) that can update an eigen-space incrementally for a sequence of data. However, in IKPCA, the eigenvalue decomposition should be carried out f...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2016
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11466/1/Online%20feature%20extraction%20based%20on%20accelerated%20kernel%20principal%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/11466/ http://link.springer.com/article/10.1007%2Fs12530-015-9131-7 |
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