We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.
History
Pagination
1 - 8
Location
Anchorage, Alaska
Open access
Yes
Start date
2008-06-23
End date
2008-06-28
ISSN
1063-6919
ISBN-13
9781424422425
ISBN-10
1424422426
Language
eng
Publication classification
E1.1 Full written paper - refereed
Copyright notice
2008, IEEE
Title of proceedings
CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition