Learning the discriminative dictionary for sparse representation by a general fisher regularized model

Document Type

Conference Proceeding

Publication Date

12-9-2015

Abstract

This paper presents two novel discriminative dictionary learning models for sparse representation, namely the Fisher discriminative sparse model (FDSM) and the marginal Fisher discriminative sparse model (MFDSM). To learn the FDSM and the MFDSM efficiently and homogeneously, a general Fisher regularized model is further derived so that both of them can be learned without much modification. Experimental results on four popular databases, namely the extended Yale face database B, the AR face database, the 15 scenes dataset and the MIT-67 indoor scenes dataset show that the proposed method can improve upon other popular methods.

Identifier

84956678355 (Scopus)

ISBN

[9781479983391]

Publication Title

Proceedings International Conference on Image Processing Icip

External Full Text Location

https://doi.org/10.1109/ICIP.2015.7351627

ISSN

15224880

First Page

4347

Last Page

4351

Volume

2015-December

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