PUlasso: High-Dimensional Variable Selection With Presence-Only Data.

ABSTRACT: In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and classification with positive and unlabeled responses. Our algorithm involves using the majorization-minimization framework which is a generalization of the well-known expectation-maximization (EM) algorithm. In particular to make our algorithm scalable, we provide two computational speed-ups to the standar

SUBMITTER: Song H 

PROVIDER: S-EPMC7133715 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature