On the Limitation of Kernel Dependence Maximization for Feature Selection

arXiv:2406.06903v1 Announce Type: cross
Abstract: 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 …

abstract arxiv criterion cs.lg feature features feature selection kernel literature math.st popular proposal schmidt simple stat.ml stat.th type