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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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GrooveSquid.com Paper Summaries

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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