NDLI: Margin-based discriminant dimensionality reduction for visual recognition

Content Provider	IEEE Xplore Digital Library
Author	Cevikalp, H. Triggs, B. Jurie, F. Polikar, R.
Copyright Year	2008
Description	Author affiliation: Eskisehir Osmangazi Univ., Eskisehir (Cevikalp, H.)
Abstract	Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the class regions densely. In such cases, test samples often fall into gaps between training samples where the nearest neighbours are too distant to be good indicators of class membership. One solution is to project the data onto a discriminative lower dimensional subspace. We propose a gap-resistant nonparametric method for finding such subspaces: first the gaps are filled by building a convex model of the region spanned by each class - we test the affine and convex hulls and the bounding disk of the class training samples - then a set of highly discriminative directions is found by building and decomposing a scatter matrix of weighted displacement vectors from training examples to nearby rival class regions. The weights are chosen to focus attention on narrow margin cases while still allowing more diversity and hence more discriminability than the 1D linear Support Vector Machine (SVM) projection. Experimental results on several face and object recognition datasets show that the method finds effective projections, allowing simple classifiers such as nearest neighbours to work well in the low dimensional reduced space.
Starting Page	1
Ending Page	8
File Size	760385
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587591
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Scattering Kernel Matrix decomposition Support vector machines Support vector machine classification Face recognition Object recognition Multidimensional systems Filling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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