Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers

Abolghasemi, V, Chen, M, Alameer, A ORCID: https://orcid.org/0000-0002-7969-3609, Ferdowsi, S, Chambers, J and Nazarpour, K 2018, 'Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers' , IEEE Signal Processing Letters, 25 (4) , pp. 472-476.

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Abstract

We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method in classification of a novel open-source database of Chinese numbers. Benchmarking results confirm that the InDPL algorithm offers enhanced classification accuracy, especially when the number of training samples is limited.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: IEEE Signal Processing Letters
Publisher: IEEE
ISSN: 1070-9908
Depositing User: A Alameer
Date Deposited: 09 Jun 2022 14:53
Last Modified: 13 Jun 2022 09:40
URI: http://usir.salford.ac.uk/id/eprint/63749

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