Personalized Convolutional Dictionary Learning of Physiological Time Series

Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre

TL;DR: A novel approach to convolutional dictionary learning is proposed for the representation of time series: a transformation function is learned during optimization to personalize the dictionary, thereby accounting for inter-individual variability.

Abstract:

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.

Submission Number: 621