Top-$k$ Regularization for Supervised Feature Selection

• URL: http://arxiv.org/abs/2106.02197v1
• Date: Fri, 4 Jun 2021 01:12:47 GMT
• Title: Top-$k$ Regularization for Supervised Feature Selection
• Authors: Xinxing Wu, Qiang Cheng
• Abstract summary: We introduce a novel, simple yet effective regularization approach, named top-$k$ regularization, to supervised feature selection. We show that the top-$k$ regularization is effective and stable for supervised feature selection.
• Score: 11.927046591097623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of domain problems. Existing methods mainly depend on feature scoring functions or sparse regularizations; nonetheless, they have limited ability to reconcile the representativeness and inter-correlations of features. In this paper, we introduce a novel, simple yet effective regularization approach, named top-$k$ regularization, to supervised feature selection in regression and classification tasks. Structurally, the top-$k$ regularization induces a sub-architecture on the architecture of a learning model to boost its ability to select the most informative features and model complex nonlinear relationships simultaneously. Theoretically, we derive and mathematically prove a uniform approximation error bound for using this approach to approximate high-dimensional sparse functions. Extensive experiments on a wide variety of benchmarking datasets show that the top-$k$ regularization is effective and stable for supervised feature selection.

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