Integrative multi-view regression: Bridging group-sparse and low-rank models.

ABSTRACT: Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. We cast this new problem under the familiar multivariate regression framework and propose an integrative reduced-rank regression (iRRR), where each view has its own low-rank coefficient matrix. As such, latent features are extracted from each view in a supervised fashion. For model estimation, we develop a convex composite nuclear norm penalization approach, which admits

SUBMITTER: Li G 

PROVIDER: S-EPMC6849205 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature