2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-13.2
Paper Title ALIGNING SETS OF TEMPORAL SIGNALS WITH RIEMANNIAN GEOMETRY AND KOOPMAN OPERATOR
Authors Ohad Rahamim, Ronen Talmon, Technion - Israel Institute of Technology, Israel
SessionSPTM-13: Models, Methods and Algorithms 1
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SSP] Statistical Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.