Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LSP.2020.3027462 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Principal component analysis, Analytical models, Image reconstruction, Signal processing algorithms, Convergence, Robustness, Principle component analysis, robust learning, attentive mechanism, image reconstruction | Journal | 27 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
15 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danyang Wu | 1 | 5 | 3.47 |
| Han Zhang | 2 | 123 | 28.55 |
| Feiping Nie | 3 | 7061 | 309.42 |
| Rong Wang | 4 | 120 | 15.54 |
| C. Yang | 5 | 296 | 43.66 |
| Xiaoxue Jia | 6 | 0 | 0.34 |
| Xuelong Li | 7 | 15049 | 617.31 |