Neurodynamics-driven supervised feature selection

•The supervised similarity measure based on information theory incorporating the information of class labels is applied to quantify similarities between features.•The proposed feature selection problem via holistic redundancy minimization based on the supervised similarity measure is mathematically formulated as a biconvex optimization problem with a quartic objective function. In addition, an iteratively reweighted convex quadratic program is reformulated.•A two-timescale duplex neurodynamic system is applied to solve the formulated biconvex optimization problem and a projection neural network is customized to solve the iteratively reweighted convex optimization problem.•The two-timescale duplex neurodynamic approach is substantiated to be almost-surely convergent to global optimal solutions to the formulated biconvex optimization problem.•Extensive experimental results on benchmark datasets demonstrate the superior performance of the proposed QWRM-based feature selection methods in comparison with the mainstream feature selection methods.