Summary of Adaptive Collaborative Correlation Learning-based Semi-supervised Multi-label Feature Selection, by Yanyong Huang et al.
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
by Yanyong Huang, Li Yang, Dongjie Wang, Ke Li, Xiuwen Yi, Fengmao Lv, Tianrui Li
First submitted to arxiv on: 18 Jun 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 Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples missing labels. The paper proposes an Adaptive Collaborative Correlation lEarning-based Semi-Supervised Multi-label Feature Selection (Access-MFS) method, which addresses issues such as noise and outliers, unknown labels, and redundancy. The Access-MFS method uses a generalized regression model equipped with an extended uncorrelated constraint to select discriminative yet irrelevant features and maintain consistency between predicted and ground-truth labels in labeled data. Additionally, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to select important features from high-dimensional multi-label data when some samples don’t have labels. The method, called Access-MFS, helps solve problems caused by noise and unknown labels. It also finds the right balance between selecting useful features and avoiding redundant ones. By combining different types of information, Access-MFS can pick better features than other methods. |
Keywords
» Artificial intelligence » Feature selection » Regression » Semi supervised
