Isotonic CCA for sequence alignment and activity recognition
This paper presents an approach for sequence alignment based on canonical correlation analysis(CCA). We show that a novel set of constraints imposed on traditional CCA leads to canonical solutions with the time warping property, i.e., non-decreasing monotonicity in time. This formulation generalizes the more traditional dynamic time warping (DTW) solutions to cases where the alignment is accomplished on arbitrary subsequence segments, optimally determined from data, instead on individual sequence samples. We then introduce a robust and efficient algorithm to find such alignments using non-negative least squares reductions. Experimental results show that this new method, when applied to MOCAP activity recognition problems, can yield improved recognition accuracy.