Learning Time-series Shapelets Enhancing Discriminability

Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima

Abstract: Shapelets are subsequences that are effective for classifying time-series instances. Joint learning of both classifiers and shapelets has recently been studied because this approach improves algorithmic complexity and classification performance. However, the existing methods lack the power of feature discrimination due to using traditional sigmoid cross-entropy loss functions. To enhance feature discriminability, we propose self-adaptive scaling of the loss functions, inspired by the recent discriminative loss in computer vision. In addition, we propose a theoretically sound regularization that enhances feature discriminability and maintains shapelet interpretability by shrinking appropriate features. Using UCR datasets, we demonstrate improved area under the curve and interpretability of shapelets with a small number of shapelets.