Double-attentive principle component analysis

Danyang Wu, Han Zhang, Feiping Nie, Rong Wang, Chao Yang, Xiaoxue Jia, Xuelong Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This letter proposes a double-attentive principle component analysis (DA-PCA) model for image processing. Compared to the previous PCA-based works that cannot deal with normal images and outliers effectively, the proposed DA-PCA model performs a double-attentive mechanism to sever the connections with outliers and hold the effectiveness of normal images. To solve the proposed DA-PCA model, we propose an efficiently iterative algorithm and provide strict convergence analysis for it.Moreover, in the simulations, we conduct the reconstruction and classification experiments on several real datasets and the experimental results demonstrate the superb performance of our proposal.

Original languageEnglish
Article number9209108
Pages (from-to)1814-1818
Number of pages5
JournalIEEE Signal Processing Letters
Volume27
DOIs
StatePublished - 2020

Keywords

  • Attentive mechanism
  • Image reconstruction
  • Principle component analysis
  • Robust learning

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