Summary of On the Limitation Of Kernel Dependence Maximization For Feature Selection, by Keli Liu and Feng Ruan
On the Limitation of Kernel Dependence Maximization for Feature Selection
by Keli Liu, Feng Ruan
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 In this paper, researchers propose an intuitive method for selecting relevant features from a dataset by maximizing a nonparametric measure of dependence between the response variable and the features. The approach uses the Hilbert-Schmidt Independence Criterion (HSIC) as the dependence measure, which is popular in the literature. However, the authors demonstrate that this method can be flawed and may miss critical features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that feature selection via HSIC maximization can lead to poor results by presenting counterexamples. This highlights the need for more careful consideration of the limitations and pitfalls of feature selection methods. |
Keywords
» Artificial intelligence » Feature selection
