Summary of Towards Robust Uncertainty-aware Incomplete Multi-view Classification, by Mulin Chen et al.
Towards Robust Uncertainty-Aware Incomplete Multi-View Classification
by Mulin Chen, Haojian Huang, Qiang Li
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Alternating Progressive Learning Network (APLN) is designed to enhance Evidential Deep Learning (EDL)-based methods for handling incomplete data in multi-view classification. By first applying coarse imputation and then mapping the data to a latent space, APLN mitigates bias from corrupted observed data. The approach incorporates uncertainty considerations through EDL and introduces a conflict-aware Dempster-Shafer combination rule (DSCR) to better handle conflicting evidence. Sampling from the learned distribution optimizes latent representations of missing views, reducing bias and enhancing decision-making robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to handle incomplete data in multi-view classification. It’s called APLN, which stands for Alternating Progressive Learning Network. This method helps by first fixing some of the missing information, and then learning more about the missing parts. The approach takes into account how uncertain things are, and it also has a special rule to deal with situations where there is conflicting evidence. This leads to better decisions being made. |
Keywords
» Artificial intelligence » Classification » Deep learning » Latent space
