Neurodynamics-driven supervised feature selection

Feature selection is an important dimensionality reduction technique in machine learning, pattern recognition, image processing, and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some feature selection methods based on global optimization of unsupervised redundancy may potentiate performance improvements, they may or may not be relevant to classification as the information on pairwise features with class labels is missing. In this paper, based on a supervised similarity measure, a biconvex optimization problem is formulated for holistic feature section with a quadratically weighted objective function subject to linear equality and nonnegativity constraints. 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. Experimental results of the proposed neurodynamics-based supervised feature selection are elaborated in comparison with several existing feature selection methods based on twenty benchmark datasets to substantiate the efficacy and superiority of the neurodynamics-based method for selecting informative features in classification.