venkatesh-jointlearning-2008.pdf (179.75 kB)
Joint learning and dictionary construction for pattern recognition
conference contribution
posted on 2008-01-01, 00:00 authored by D S Pham, Svetha VenkateshSvetha VenkateshWe 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
Event
IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)Pagination
1 - 8Publisher
IEEELocation
Anchorage, AlaskaPlace of publication
Washington, D. C.Start date
2008-06-23End date
2008-06-28ISSN
1063-6919ISBN-13
9781424422425ISBN-10
1424422426Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern RecognitionUsage metrics
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