通过符号-时间一致性自监督学习，提升医疗时序数据在行为变化下的分类鲁棒性。
Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification
- 利用符号化表示捕捉时序不变特征，增强对数据扭曲的抵抗能力。
- 在存在显著数据分布偏移时，分类准确率显著优于基线方法。
- 适合处理噪声大、概念漂移严重的数字健康时序数据任务。
数字健康领域中时序数据的重要性日益凸显，亟需先进方法提取有意义的模式与表征。自监督对比学习已成为直接从原始数据中学习的有前景方法。然而，数字健康时序数据通常高度噪声、存在概念漂移，给训练泛化能力强的深度学习模型带来挑战。本文聚焦于由不同人类行为引起的数据分布偏移问题，提出一种感知符号袋（bag-of-symbol）表示的自监督学习框架。该表示对时序数据的形变、位置偏移和噪声不敏感，有助于引导深度模型学习对数据偏移具有鲁棒性的表征。实验表明，所提方法在存在显著数据偏移时性能显著更优。
原文摘要 · Abstract (English)
The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity to data warping, location shifts, and noise existed in time series data, making it potentially pivotal in guiding deep learning to acquire a representation resistant to such data shifting. We demonstrate that the proposed method can achieve significantly better performance where significant data shifting exists.
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