Summary of Conden-fi: Consistency and Diversity Learning-based Multi-view Unsupervised Feature and In-stance Co-selection, by Yanyong Huang et al.
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection
by Yanyong Huang, Yuxin Cai, Dongjie Wang, Xiuwen Yi, Tianrui Li
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 multi-view unsupervised feature and instance co-selection method, CONDEN-FI, tackles the challenge of simultaneously selecting representative features and instances from unlabeled data. By reconstructing multi-view data in both sample and feature spaces, CONDEN-FI learns consistent representations across views while capturing view-specific information. This enables the selection of important features and instances. The algorithm also learns a view-consensus similarity graph to select diverse instances. Experimental results on real-world datasets demonstrate CONDEN-FI’s effectiveness compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to pick the most important features and samples from different views of data without any labels. This is helpful for tasks that use these features, like image recognition or natural language processing. The method, called CONDEN-FI, reconstructs the data in two ways: one by feature and one by sample. This helps it learn what’s important across all the views. It also selects diverse samples to include. Tests on real datasets show this approach works well. |
Keywords
» Artificial intelligence » Natural language processing » Unsupervised
