Peak Persistence Diagrams for Shape-Based Signal Estimation.

ABSTRACT: Signal estimation from noisy data is a fundamental problem in signal processing and data analysis. Existing literature offers various estimators based on different model choices and estimation criteria. This paper uses an innovative framework that leverages topological and geometric features of the data for signal estimation. The proposed approach builds on penalized elastic signal alignment (PESA) framework and introduces a topological tool - peak-persistence diagram (PPD) - to analyze shapes of signals over the range of potential alignments. In the first step, the PPD estimates the unknown signal shape, defined as the number of internal peaks and valleys. In the second step, we estimate the underlying signal using shape-constrained optimization. This PESA approach strikes a balanc

SUBMITTER: Kim WM 

PROVIDER: S-EPMC12803482 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature