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

Abolghasemi, V; Chen, M; Alameer, A; Ferdowsi, S; Chambers, J; Nazarpour, K

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.

Citation

Abolghasemi, V., Chen, M., Alameer, A., Ferdowsi, S., Chambers, J., & Nazarpour, K. (2018). Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers. IEEE Signal Processing Letters, 25(4), 472-476. https://doi.org/10.1109/LSP.2018.2798406

• Journal Article Type: Article
• Publication Date: Jan 25, 2018
• Deposit Date: Jun 9, 2022
• Publicly Available Date: Jun 9, 2022
• Journal: IEEE Signal Processing Letters
• Print ISSN: 1070-9908
• Electronic ISSN: 1558-2361
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 25
• Issue: 4
• Pages: 472-476
• DOI: https://doi.org/10.1109/LSP.2018.2798406
• Publisher URL: https://ieeexplore-ieee-org.salford.idm.oclc.org/document/8269282