Shape averaging under Time Warping

Dynamic Time Warping (DTW) distance measure has increasingly been used as a similarity measurement for various data mining tasks in place of traditional Euclidean distance metric due to its superiority in sequence-alignment flexibility. However, in some tasks where shape averaging is required, e.g., in template matching and k-means clustering problems, current averaging methods are inaccurate in that they produce undesired templates and cluster representatives. In this work, we emphasize the importance of the correctness of this averaging subroutine and propose a novel shape averaging method, called Prioritized Shape Averaging (PSA), using hierarchical clustering approach. In experimental evaluation, our proposed method, PSA, achieves a lower discrepancy distance between an averaged sequence and every original sequence than existing method on various domains.