Experimental Design on a Budget for Sparse Linear Models and Applications.

ABSTRACT: Budget constrained optimal design of experiments is a well studied problem. Although the literature is very mature, not many strategies are available when these design problems appear in the context of sparse linear models commonly encountered in high dimensional machine learning. In this work, we study this budget constrained design where the underlying regression model involves a ℓ₁-regularized linear function. We propose two novel strategies: the first is motivated geometrically whereas the second is algebraic in nature. We obtain tractable algorithms for this problem which also hold for a more general class of sparse linear models. We perform a detailed set of experiments, on benchmarks and a large neuroimaging study, showing that the proposed models are effective in

SUBMITTER: Ravi SN 

PROVIDER: S-EPMC5415092 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature