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Time-frequency Based Multi-task Learning for Semi-supervised Time Series Classification by Chixuan Wei; Zhihai Wang; Jidong Yuan; Chuanming Li; Shengbo Chen is a Computer Science article available to read on EtoBox.

What is Time-frequency Based Multi-task Learning for Semi-supervised Time Series Classification about?

Semi-supervised learning is crucial for alleviating labeling burdens in time series classification. Most of the existing semi-supervised time series classification methods extract patterns from the time domain, ignoring the time-frequency domain and the latent feature space shared by the labeled and unlabeled samples. For that, a Multi-task learning scheme based on Time-Frequency mining for semi-supervised time series Classification (MTFC) is proposed. First, we design a series of unsupervised tasks for capturing time-frequency information. Considering the consistency between labeled and unlabeled data, we then employ a multi-task learning framework to learn their common features. Meanwhile, we theoretically analyze the proposed semi-supervised classification method and provide a novel generalization result for the MTFC. Extensive experiments on multiple time series datasets demonstrate that our MTFC can effectively improve the performance of semi-supervised classification and achieve state-of-the-art results.

Who reads Time-frequency Based Multi-task Learning for Semi-supervised Time Series Classification?

Author

Chixuan Wei; Zhihai Wang; Jidong Yuan; Chuanming Li; Shengbo Chen

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)