Classification via Incoherent Subspaces

Details Classification via Incoherent Subspaces Classification via Incoherent Subspaces

2010-05-10

arxiv.org/abs/1005.1471v1

Computer Science

Computer Vision and Pattern Recognition

22 pages, 2 figures, 4 tables

Scientific paper

This article presents a new classification framework that can extract individual features per class. The scheme is based on a model of incoherent subspaces, each one associated to one class, and a model on how the elements in a class are represented in this subspace. After the theoretical analysis an alternate projection algorithm to find such a collection is developed. The classification performance and speed of the proposed method is tested on the AR and YaleB databases and compared to that of Fisher's LDA and a recent approach based on on $\ell_1$ minimisation. Finally connections of the presented scheme to already existing work are discussed and possible ways of extensions are pointed out.

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