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Double-Attentive Principle Component Analysis
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DOI：10.1109/LSP.2020.3027462.png)
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Abstract
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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.
Keywords:
Principal component analysis
Analytical models
Image reconstruction
Signal processing algorithms
Convergence
Robustness
Principle component analysis
robust learning
attentive mechanism
image reconstruction
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