Classification via Incoherent Subspaces

05/10/2010

∙

by   Karin Schnass, et al.

∙

0

∙

share

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 ℓ_1 minimisation. Finally connections of the presented scheme to already existing work are discussed and possible ways of extensions are pointed out.