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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