The Cluster Elastic Net for High-Dimensional Regression With Unknown Variable Grouping.

ABSTRACT: In the high-dimensional regression setting, the elastic net produces a parsimonious model by shrinking all coefficients towards the origin. However, in certain settings, this behavior might not be desirable: if some features are highly correlated with each other and associated with the response, then we might wish to perform less shrinkage on the coefficients corresponding to that subset of features. We propose the cluster elastic net, which selectively shrinks the coefficients for such variables towards each other, rather than towards the origin. Instead of assuming that the clusters are known a priori, the cluster elastic net infers clusters of features from the data, on the basis of correlation among the variables as well as association with the response. These clusters ar

SUBMITTER: Witten DM 

PROVIDER: S-EPMC4011669 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature