Kernel Cyclicity Analysis

Datum

10.02.2026

Zeit

15:00 - 16:00

Sprecher

Darrick Lee

Zugehörigkeit

University of Edinburgh

Sprache

en

Hauptthema

Biologie

Host

Kelly Maggs

Beschreibung

Time series data often contain latent cyclic structure, but standard tools to recover it often assume regular sampling and reliable time stamps. Cyclicity analysis was introduced as a reparametrization invariant method to detect linear planes which maximize the signed area of time series, acting as a proxy for cyclic behavior. In this talk, we discuss ongoing work to generalize cyclicity analysis to the nonlinear setting by using kernel methods. We introduce computable method to extract nonlinear cyclic coordinates from time series along with geometric phase variables. We’ll discuss some theoretical results, along with some preliminary experiments.

Letztmalig verändert: 08.02.2026, 07:39:25