Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an incre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2012
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| Online Access: | http://eprints.nottingham.ac.uk/31426/ http://eprints.nottingham.ac.uk/31426/ http://eprints.nottingham.ac.uk/31426/ http://eprints.nottingham.ac.uk/31426/1/tzimiroTNN12b.pdf |