Summary of Embedded Multi-label Feature Selection Via Orthogonal Regression, by Xueyuan Xu et al.
Embedded Multi-label Feature Selection via Orthogonal Regression
by Xueyuan Xu, Fulin Wei, Tianyuan Jia, Li Zhuo, Feiping Nie, Xia Wu
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a novel embedded multi-label feature selection method called Global Redundancy and Relevance Optimization in Orthogonal Regression (GRROOR) to tackle the challenge of preserving sufficient discriminative information in multi-label data. The approach employs orthogonal regression with feature weighting, considering both global feature redundancy and label relevancy information. The cost function is an unbalanced orthogonal Procrustes problem on the Stiefel manifold, solved using a simple yet effective scheme. Experimental results on ten multi-label datasets demonstrate the effectiveness of GRROOR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to choose which features are most important in multi-label data. This helps with tasks like predicting multiple labels at once. The method uses a special kind of regression that considers how well different features relate to each other and the labels. It also tries to remove redundant features, so you’re not wasting time on unimportant information. The results show that this approach is better than others at finding the most useful features. |
Keywords
» Artificial intelligence » Feature selection » Optimization » Regression
