Linear dynamical based models for sequential domains

Luca Pasa, Alessandro Sperduti, Peter Tiño

Abstract: The aim of the paper is to explore how models based on a linear dynamic can be used in order to perform a prediction task in sequential domains. In the literature, it has already been shown that Linear Dynamical Systems (LDSs) can be quite useful when dealing with sequence learning tasks. Our aim is to study whether it is possible to use LDSs as building blocks for constructing more complex and powerful models. Specifically, we propose a model dubbed Linear System Network, that exploits several LDSs in order to compute a nonlinear projection of the input. Moreover, we explore whether is it possible to apply a co-learning technique in order to improve the performance of LDSs for the considered prediction task.