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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| Format: | Article |
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IEEE
2012
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| Online Access: | https://eprints.nottingham.ac.uk/31426/ |
| _version_ | 1848794198652747776 |
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| author | Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja |
| author_facet | Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja |
| author_sort | Liwicki, Stephan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T19:12:23Z |
| format | Article |
| id | nottingham-31426 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:23Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314262020-05-04T16:34:08Z https://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 Liwicki, Stephan, Zafeiriou, Stefanos, 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 Face Recognition Gradient Methods Learning (Artificial Intelligence) Object Tracking Principal Component Analysis http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269106 doi:10.1109/TNNLS.2012.2208654 doi:10.1109/TNNLS.2012.2208654 |
| spellingShingle | Face Recognition Gradient Methods Learning (Artificial Intelligence) Object Tracking Principal Component Analysis Liwicki, Stephan Zafeiriou, Stefanos Tzimiropoulos, Georgios Pantic, Maja Efficient online subspace learning with an indefinite kernel for visual tracking and recognition |
| title | 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_short | 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 |
| topic | Face Recognition Gradient Methods Learning (Artificial Intelligence) Object Tracking Principal Component Analysis |
| url | https://eprints.nottingham.ac.uk/31426/ https://eprints.nottingham.ac.uk/31426/ https://eprints.nottingham.ac.uk/31426/ |