Title: A Hybrid Convolutionary Neural Network and Low-rank Tensor Learning Algorithm for Tensor-on-Tensor Regression

Abstract:

The problem of predicting a set of tensorial outputs based on inputs of tensor form has been receiving increasing attention in recent years. This problem arises in various areas of mathematical, statistical, and computational sciences, and generalizes the case of the widely used scalar-on-scalar regression methods. In this paper, we develop a tensor-on-tensor regression framework using a hybrid of coevolutionary neural networks and low-rank tensor learning algorithms. Our proposed framework integrates several promising approaches which have been developed previously to tackle this problem and extends their domain of applications. In particular, we demonstrate the advantage of this framework in comparison with traditional methods through an example of predicting the third-order tensors which arise within the procedures required for performing the time-homogeneous top-K ranking algorithm. Computational results are further provided which pertain to the analysis of the U.S. stock market during the time period from January 1990 to December 2019.

Biography:

Masoud Atael is a Solution Specialist at SAS and completed masters at York University, Canada.