Sparse Minimum Redundancy Maximum Relevance for Feature Selection

Peter Naylor

doi:10.1111/sjos.70074

Sparse Minimum Redundancy Maximum Relevance for Feature Selection

2026-09-01

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Héctor Climente-González

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Makoto Yamada

DOI：10.1111/sjos.70074

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Abstract

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We propose a feature screening method that integrates both feature-feature and feature-target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classical mRMR penalized by a non-convex regularizer, and where the parameters estimated as zero coefficients represent the set of inactive features. We establish the conditions under which zero coefficients are correctly identified to guarantee accurate recovery of inactive features. We introduce a multi-stage procedure based on the knockoff filter that enables the penalized mRMR to discard inactive features while controlling the false discovery rate (FDR). Our method performs comparably to HSIC-LASSO but is more conservative in the number of selected features. It only requires setting an FDR threshold, rather than specifying the number of features to retain. The effectiveness of the method is illustrated through simulations and real-world datasets. The code to reproduce this work is available on the following GitHub: https://github.com/PeterJackNaylor/SmRMR.

Keywords:

FDR

feature screening

mRMR

redundancy

sparsistency

Sparse Minimum Redundancy Maximum Relevance for Feature Selection

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