1. A computer-implemented method for relevance detection and dimensionality reduction in a big data parallelized computing environment to effectively minimize selection of redundant features, the method comprising: accessing a first set of features, wherein the first set of features includes multiple features, wherein the features of the first set of features are characterized by a variance measure, and wherein accessing the first set of features includes using a computing system to access the features; determining components of a covariance matrix, the components of the covariance matrix indicating a covariance with respect to pairs of features in the first set; selecting multiple features from the first set of features in a first iteration, wherein selecting multiple features in the first iteration is based on the determined components of the covariance matrix and an amount of the variance measure attributable to the selected multiple features; determining relevance scores for the selected multiple features from the first set of features in the first iteration; determining exactly one feature of the selected multiple features in the first iteration, wherein the single feature has the highest relevance score of the selected multiple features; and selecting multiple features of a second set of features in a second iteration, wherein the second set of features includes the determined single feature.