@inproceedings{7fb2ffa5abd840e987e5a827fc33fb15,
title = "Aligning Sets of Temporal Signals with Riemannian Geometry and Koopman Operator",
abstract = "In this paper, we consider the problem of aligning data sets of short temporal signals without any a-priori known correspondence. We present a method combining Koopman operator theory and the Riemannian geometry of symmetric positive-definite (SPD) matrices. First, by taking a Koopman operator theory standpoint, we build feature matrices of the signals using dynamic mode decomposition (DMD). Second, we align these features using parallel transport of SPD matrices, built from the DMD feature matrices. We showcase the performance of the proposed method on simulated observations of a mechanical system and on two real-world applications: sleep stage identification and pre-epileptic seizure prediction.",
keywords = "Domain adaptation, Dynamic mode decomposition, Koopman operator, Parallel transport, Riemannian geometry",
author = "Ohad Rahamim and Ronen Talmon",
note = "Publisher Copyright: {\textcopyright}2021 IEEE.; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
doi = "10.1109/icassp39728.2021.9413729",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5310--5314",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
address = "الولايات المتّحدة",
}