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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Main Authors:	Liwicki, Stephan, Zafeiriou, Stefanos, Tzimiropoulos, Georgios, Pantic, Maja
Format:	Article
Language:	English
Published:	IEEE 2012
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

id	nottingham-31426
recordtype	eprints
spelling	nottingham-314262017-10-14T08:58:54Z http://eprints.nottingham.ac.uk/31426/ Efficient online subspace learning with an indefinite kernel for visual tracking and recognition Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja 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. IEEE 2012-09-10 Article PeerReviewed application/pdf en http://eprints.nottingham.ac.uk/31426/1/tzimiroTNN12b.pdf 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. Neural Networks and Learning Systems, IEEE Transactions on, 23 (10). pp. 1624-1636. ISSN 2162-237X http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269106 doi:10.1109/TNNLS.2012.2208654 doi:10.1109/TNNLS.2012.2208654
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
description	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.
format	Article
author	Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja
spellingShingle	Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
author_facet	Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja
author_sort	Liwicki, Stephan
title	Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
title_short	Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
title_full	Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
title_fullStr	Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
title_full_unstemmed	Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
title_sort	efficient online subspace learning with an indefinite kernel for visual tracking and recognition
publisher	IEEE
publishDate	2012
url	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
first_indexed	2018-09-06T12:08:40Z
last_indexed	2018-09-06T12:08:40Z
_version_	1610859907216572416

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

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