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

Vahid Abolghasemi, Mingyang Chen, Ali Alameer, Saideh Ferdowsi, Jonathon Chambers, Kianoush Nazarpour

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)472 - 476
Number of pages5
JournalIEEE Signal Processing Letters
Volume25
Issue number4
Early online date25 Jan 2018
DOIs
Publication statusPublished - 1 Apr 2018

Keywords

  • Chinese numbers
  • classification
  • incoherent dictionary pair learning

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