Aligning Sets of Temporal Signals with Riemannian Geometry and Koopman Operator

Ohad Rahamim, Ronen Talmon

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)5310-5314
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2021-June
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Bibliographical note

Publisher Copyright:
©2021 IEEE.

Funding

This project has received funding from the European Union’s Horizon 2020 research grant agreement No 802735-ERC-DIFFOP.

FundersFunder number
Horizon 2020 Framework Programme802735-ERC-DIFFOP

    Keywords

    • Domain adaptation
    • Dynamic mode decomposition
    • Koopman operator
    • Parallel transport
    • Riemannian geometry

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