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Abstract: 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... (Update)
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BibTeX entry: (Update)
T-K Kim, O. Arandjelovi c and R. Cipolla, Learning over Sets using Boosted Manifold Principal Angles (BoMPA). BMVC, 2005. http://citeseer.ist.psu.edu/kim05learning.html More
@misc{ kim05learning,
author = "T. Kim and O. Arandjelovi and c Cipolla",
title = "Learning over Sets using Boosted Manifold Principal Angles",
text = "T-K Kim, O. Arandjelovi c and R. Cipolla, Learning over Sets using Boosted
Manifold Principal Angles (BoMPA). BMVC, 2005.",
year = "2005",
url = "citeseer.ist.psu.edu/kim05learning.html" }
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