Efficient online subspace learning with an indefinite kernel for visual tracking and recognition

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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...

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Bibliographic Details
Main Authors:	Liwicki, Stephan, Zafeiriou, Stefanos, Tzimiropoulos, Georgios, Pantic, Maja
Format:	Article
Published:	IEEE 2012
Subjects:	Face Recognition Gradient Methods Learning (Artificial Intelligence) Object Tracking Principal Component Analysis
Online Access:	https://eprints.nottingham.ac.uk/31426/

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