Given a motion capture sequence, how to identify the category of the motion? Classifying human motions is a critical task in motion editing and synthesizing, for which manual labeling is clearly inecient for large databases. Here we study the general problem of time series clustering. We propose a novel method of clustering time series that can (a) learn joint temporal dynamics in the data; (b) handle time lags; and (c) produce interpretable features. We achieve this by developing complex-valued linear dynamical systems (CLDS), which include real-valued Kalman lters as a special case; our advantage is that the transition matrix is simpler (just diagonal), and the transmission one easier to interpret. We then present ComplexFit, a novel EM algorithm to learn the parameters for the general model and its special case for clustering. Our approach produces signicant improvement in clustering quality, 1.5 to 5 times better than well-known competitors on real motion capture sequences.
['Lei Li', 'B. A. Prakash']
Time Series Clustering: Complex is Simpler!