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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2016
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11466/ http://ir.unimas.my/id/eprint/11466/1/Online%20feature%20extraction%20based%20on%20accelerated%20kernel%20principal%20%28abstract%29.pdf |