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SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
[article]
2021
arXiv
pre-print
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
arXiv:2110.00578v2
fatcat:lvplo2auovdfzka66yaghsvi5q