4.6 Article

Deep Multiple Metric Learning for Time Series Classification

期刊

IEEE ACCESS
卷 9, 期 -, 页码 17829-17842

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3053703

关键词

Measurement; Time series analysis; Training; Feature extraction; Task analysis; Neural networks; Learning systems; Adversarial training; deep learning; metric learning; time series classification

资金

  1. National Key RAMP
  2. D Program of China [2019YFC1710300, SQ2018YFC200065-02]
  3. Sichuan Science and Technology Program [2021YJ0184, 2020YFS0283, 2020YFS0302, 2019YFS0019]

向作者/读者索取更多资源

The study proposes a novel deep multiple metric learning (DMML) method for time series classification. It utilizes a convolutional network to extract nonlinear features of time series and builds multiple metric learners to obtain multiple metrics for exploiting locality information. An adversarial negative generator and an auxiliary loss are introduced to increase the robustness of the method for the magnitude of distance.
Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification. However, most existing approaches focus on learning a single linear metric, which is unsuitable for nonlinear relationships and heterogeneous datasets with locality information. Besides, the hard samples in the training set account for only a small part, which may fail to characterize the global geometry of the metric embedding space. In this paper, we propose a novel deep multiple metric learning (DMML) method for time series classification. DMML contains a convolutional network component to extract nonlinear features of time series. For exploiting locality information, the last feature layer of the convolutional network is divided into several nonoverlapping groups and a separate metric learner is built on each group to get multiple metrics. In order to reduce the correlations among learners and facilitate robust metric learning, we design an adversarial negative generator to synthesize different hard negative complements for different metric learners. Moreover, an auxiliary loss is introduced to increase the robustness of DMML for the magnitude of distance. Extensive experiments on UCR datasets demonstrate the effectiveness of DMML for time series classification.

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