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Learning over sets using boosted manifold principal angles (BoMPA)

Kim, Tae-Kyun, Arandjelovic, Ognjen and Cipolla, Roberto 2005, Learning over sets using boosted manifold principal angles (BoMPA), in BMVC 2005 : Proceedings of the British Machine Conference 2005, BMVA Press, Manchester, England, pp. 58-1 (779)-58-10 (788), doi: 10.5244/C.19.58.

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Title Learning over sets using boosted manifold principal angles (BoMPA)
Author(s) Kim, Tae-Kyun
Arandjelovic, Ognjen
Cipolla, Roberto
Conference name British Machine Vision. Conference (2005 : Oxford, England)
Conference location Oxford, England
Conference dates 5-8 Sept. 2005
Title of proceedings BMVC 2005 : Proceedings of the British Machine Conference 2005
Editor(s) Clocksin, W F
Fitzgibbon, A W
Torr, P H S
Publication date 2005
Conference series British Machine Vision Conference
Start page 58-1 (779)
End page 58-10 (788)
Publisher BMVA Press
Place of publication Manchester, England
Summary In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.
ISBN 01901725294
Language eng
DOI 10.5244/C.19.58
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, BMVA
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058442

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.