On the Limitation of Kernel Dependence Maximization for Feature Selection
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909221024432128 |
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| author | Liu, Keli Ruan, Feng |
| author_facet | Liu, Keli Ruan, Feng |
| contents | A simple and intuitive method for feature selection consists of choosing the feature subset that maximizes a nonparametric measure of dependence between the response and the features. A popular proposal from the literature uses the Hilbert-Schmidt Independence Criterion (HSIC) as the nonparametric dependence measure. The rationale behind this approach to feature selection is that important features will exhibit a high dependence with the response and their inclusion in the set of selected features will increase the HSIC. Through counterexamples, we demonstrate that this rationale is flawed and that feature selection via HSIC maximization can miss critical features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06903 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Limitation of Kernel Dependence Maximization for Feature Selection Liu, Keli Ruan, Feng Machine Learning Statistics Theory A simple and intuitive method for feature selection consists of choosing the feature subset that maximizes a nonparametric measure of dependence between the response and the features. A popular proposal from the literature uses the Hilbert-Schmidt Independence Criterion (HSIC) as the nonparametric dependence measure. The rationale behind this approach to feature selection is that important features will exhibit a high dependence with the response and their inclusion in the set of selected features will increase the HSIC. Through counterexamples, we demonstrate that this rationale is flawed and that feature selection via HSIC maximization can miss critical features. |
| title | On the Limitation of Kernel Dependence Maximization for Feature Selection |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2406.06903 |