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Communication Dans Un Congrès Année : 2021

SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series

Résumé

Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.
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Dates et versions

hal-04371666 , version 1 (03-01-2024)

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Paternité - Partage selon les Conditions Initiales

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Citer

Jingwei Zuo, Karine Zeitouni, Yehia Taher. SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series. 2021 IEEE International Conference on Data Mining (ICDM), Dec 2021, Auckland, Australia. pp.1565-1570, ⟨10.1109/ICDM51629.2021.00206⟩. ⟨hal-04371666⟩
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