Supervised Tensor Decomposition with Interactive Side Information

• Activity Number: 344 - Student Paper Award and John M. Chambers Statistical Software Award
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Computing
• Author(s): Jiaxin Hu* and Chanwoo Lee and Miaoyan Wang
• Companies: Department of Statistics, University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison
• Keywords: Applications and case studies; Tensor data analysis; Supervised dimension reduction; Exponential family distribution; Generalized multilinear model

Abstract

We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas.