Title

Stable Discriminative Dictionary Learning Via Discriminative Deviation

Abstract

Discriminative learning of sparse-code based dictionaries tends to be inherently unstable. We show that using a discriminative version of the deviation function to learn such dictionaries leads to a more stable formulation that can handle the reconstruction/discrimination trade-off in a principled manner. Results on Graz02 and UCF Sports datasets validate the proposed formulation. © 2012 ICPR Org Committee.

Publication Date

12-1-2012

Publication Title

Proceedings - International Conference on Pattern Recognition

Number of Pages

3224-3227

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84874564098 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84874564098

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