Online Program Home
My Program

Abstract Details

Activity Number: 43 - Discovering Homology in Multi-View Data: New Statistical Methods for Data Integration
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #326563 Presentation
Title: Integrated Reduced-Rank Models with Multiple Sets of Predictors
Author(s): Gen Li* and Kun Chen
Companies: Columbia University and University of Connecticut
Keywords: Multi-view data; data integration; reduced rank regression; ADMM; nuclear norm

We introduce an integrated reduced-rank framework for multivariate regression. Predictors are multi-view data, which naturally form different groups. Each predictor group has its unique low-rank coefficient matrix. The framework flexibly captures the relationship between multivariate responses and predictors, and subsumes many existing methods such as reduced rank regression and group lasso as special cases. We develop an efficient alternating direction method of multipliers (ADMM) algorithm for model fitting, and exploit a majorization approach to deal with binary responses or missing values in responses. We demonstrate the efficacy of the proposed methods with simulation studies and a real application to the Longitudinal Study of Aging. Theoretical properties are also studied.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program