Systems and Methods for Providing a Unified Variable Selection Approach Based on Variance Preservation

This disclosure describes a method, system and computer-program product for parallelized feature selection. The method, system and computer-program product may be used to access a first set of features, wherein the first set of features includes multiple features, wherein the features are characterized by a variance measure, and wherein accessing the first set of features includes using a computing system to access the features, determine components of a covariance matrix, the components of the covariance matrix indicating a covariance with respect to pairs of features in the first set, and select multiple features from the first set, wherein selecting is based on the determined components of the covariance matrix and an amount of the variance measure attributable to the selected multiple features, and wherein selecting the multiple features includes executing a greedy search performed using parallelized computation.