Dynamic mode decomposition using supervised principal component analysis

Abstract

Dynamic mode decomposition(DMD) is a data-driven method for representing high-dimensional, nonlinear dynamical systems. DMD extracts key low-rank spatiotemporal features of the high-dimensional systems. However, since DMD is an unsupervised method and, thus, cannot incorporate label information into it even when such information is available. In this paper, we propose a framework to incorporate supervised information into DMD analyses. Experimental results show the effectiveness of performing classication tasks using modes obtained by the proposed method.