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

Liwicki, Stephan and Zafeiriou, Stefanos and Tzimiropoulos, Georgios and Pantic, Maja (2012) Efficient online subspace learning with an indefinite kernel for visual tracking and recognition. IEEE Transactions on Neural Networks and Learning Systems, 23 (10). pp. 1624-1636. ISSN 2162-237X

Full content URL: http://dx.doi.org/10.1109/TNNLS.2012.2208654

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Item Type:Article
Item Status:Live Archive

Abstract

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 incremental
KPCA in Krein space that does not require the calculation of
preimages and therefore is both efficient and exact. Our approach
has been motivated by the application of visual tracking for
which we wish to employ a robust gradient-based kernel. We
use the proposed nonlinear appearance model learned online via
KPCA in Krein space for visual tracking in many popular and
difficult tracking scenarios. We also show applications of our
kernel framework for the problem of face recognition.

Additional Information: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 incremental KPCA in Krein space that does not require the calculation of preimages and therefore is both efficient and exact. Our approach has been motivated by the application of visual tracking for which we wish to employ a robust gradient-based kernel. We use the proposed nonlinear appearance model learned online via KPCA in Krein space for visual tracking in many popular and difficult tracking scenarios. We also show applications of our kernel framework for the problem of face recognition.
Keywords:kernel methods, face recognition, gradient methods, learning (artificial intelligence), object tracking, principal component analysis
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:7450
Deposited On:07 Feb 2013 09:31

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