B0371
Title: Covariate-adjusted tensor classification in high-dimensions
Authors: Xin Zhang - Florida State University (United States) [presenting]
Abstract: In contemporary scientific research, it is of great interest to predict a categorical response based on a high-dimensional tensor and additional covariates. We introduce the CATCH model (in short for Covariate-Adjusted Tensor Classification in High-dimensions), that efficiently integrates the covariates and the tensor to predict the categorical outcome and jointly explains the relationships among the covariates, the tensor predictor, and the categorical response. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a group penalized approach and an efficient algorithm. Theoretical results confirm that our method achieves variable selection consistency and optimal prediction, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulation studies, a colorimetric sensor array data, and two neuroimaging studies.
