DeepJAM: Joint alignment of multivariate quasi-periodic functional data using deep learning

• Published in: Statistics and computing Vol. 35; no. 5
• Main Authors: Pham, Vi Thanh, Nielsen, Jonas Bille, Kofoed, Klaus Fuglsang, Kühl, Jørgen Tobias, Jensen, Andreas Kryger
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2025; Springer Nature B.V
• Subjects: Alignment; Amplitudes; Artificial Intelligence; Artificial neural networks; Computer Science; Electrocardiography; Machine learning; Multivariate analysis; Original Paper; Periodic functions; Probability and Statistics in Computer Science; Statistical analysis; Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Wearable technology; Elastic Phase-Amplitude Separation; Functional Data; Quasi-Periodic Functions; Multiscale Time Warping; Joint Multivariate Alignment; Deep Learning

The joint alignment of multivariate functional data plays an important role in various fields such as signal processing, neuroscience and medicine, including the statistical analysis of data from wearable devices. Traditional methods often ignore the phase variability and instead focus on the variab...