Hierarchical Clustering of Dynamical Systems Based on Eigenvalue Constraints

Abstract. This paper addresses the clustering problem of hidden dynamical systems behind observed multivariate sequences by assuming an interval-based temporal structure in the sequences. Hybrid dynamical systems that have transition mechanisms between multiple linear dynamical systems have become common models to generate and analyze complex time-varying event. Although the system is a flexible model for human motion and behaviors, the parameter estimation problem of the system has a paradoxical nature: temporal segmentation and system identification should be solved simultaneously. The EM algorithm is a well-known method that solves this kind of paradoxical problem; however the method strongly depends on initial values and often converges to a local optimum. To overcome the problem, we propose a hierarchical clustering method of linear dynamical systems by constraining eigenvalues of the systems. Due to the constraints, the method enables parameter estimation of dynamical systems f...

• Type: Conference
• Year: 2005
• Where: ICAPR
• Authors: Hiroaki Kawashima, Takashi Matsuyama
• Keywords: Dynamical Systems, Hidden Dynamical Systems, ICAPR 2005, Linear Dynamical Systems