Sparse nonnegative dynamic mode decomposition

Abstract

Dynamic mode decomposition (DMD) is a method to extract coherent modes from nonlinear dynamical systems. In this paper, we propose an extension of DMD, sparse nonnegative DMD, which generates a nonlinear and sparse modal representation of dynamics. In particular, this makes DMD more suitable for video processing. We reformulate DMD as a block-multiconvex optimization problem to impose constraints and regularizations directly on the structures of the estimated dynamic modes. We introduce the results of experiments with synthetic data and a surveillance video dataset and show that sparse nonnegative DMD can extract part-based dynamic modes from video streams.