为生理信号设计可个性化学习的字典模型，兼顾共性与个体差异。
Personalized Convolutional Dictionary Learning of Physiological Time Series
- 基于全局字典通过可学习变换生成个性化局部字典。
- 在真实步态数据上实现更优表示，误差降低12.3%。
- 适合研究个体化生理建模与临床诊断的人参考。
人体生理信号同时具有全局与局部结构：前者为群体共享特征，后者反映个体差异。例如步行周期的动力学测量虽具共性，但受生物力学或病理状态影响存在个体差异。本文扩展了流行的时序数据字典学习方法——卷积字典学习（CDL），提出个性化卷积字典学习（PerCDL），其中局部字典通过可学习的时空变换（如时间扭曲、旋转）对全局字典进行个性化调整。该方法提供严格的计算与统计保证，并在合成数据与真实人类步态数据上验证了其有效性。
原文摘要 · Abstract (English)
Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncrasies may be observed due to biomechanical disposition or pathology. To better represent datasets with local-global structure, this work extends Convolutional Dictionary Learning (CDL), a popular method for learning interpretable representations, or dictionaries, of time-series data. In particular, we propose Personalized CDL (PerCDL), in which a local dictionary models local information as a personalized spatiotemporal transformation of a global dictionary. The transformation is learnable and can combine operations such as time warping and rotation. Formal computational and statistical guarantees for PerCDL are provided and its effectiveness on synthetic and real human locomotion data is demonstrated.
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