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

Ali Alameer, Vahid Abolghasem

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
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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.
Original languageUndefined/Unknown
Pages (from-to)472-476
Number of pages5
JournalIEEE Signal processing Letters
Volume25
Issue number4
DOIs
Publication statusPublished - Apr 2018

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