Ellipsoidal Filtration for Topological Denoising of Recurrent Signals

Abstract

We introduce ellipsoidal filtration, a novel method for persistent homology, and demonstrate its effectiveness in denoising recurrent signals. Unlike standard Rips filtrations, which use isotropic neighbourhoods and ignore the signal's direction of evolution, our approach constructs ellipsoids aligned with local gradients to capture trajectory flow. The death scale of the most persistent H_1 feature defines a data-driven neighbourhood for averaging. Experiments on synthetic signals show that our method achieves better noise reduction than both topological and moving-average filters, especially for low-amplitude components.

Accepted for presentation at the 2025 International Symposium on Nonlinear Theory and Its Applications (NOLTA 2025), Okinawa, Japan. Originally submitted on 14 April 2025 and accepted on 16 June 2025