Sobolev-Regularized Objective Functions for Robust Pairwise Alignment of Functional Data

TL;DR

This paper introduces Sobolev-regularized objective functions for robust pairwise functional data alignment, avoiding derivative dependence and ensuring strictly monotonic warping functions through a geometric, noise-resistant approach.

Contribution

It proposes a novel Sobolev-regularized framework operating in the original function space, with theoretical guarantees and practical efficiency for functional data registration.

Findings

The framework ensures strictly monotonic, valid diffeomorphisms.

It demonstrates robustness to additive noise compared to derivative-based methods.

The approach is computationally scalable and theoretically grounded.

Abstract

Functional data registration is a critical challenge in modern statistics, essential for separating phase variability from amplitude variability. While derivative-based frameworks offer mathematically elegant solutions, their dependence on signal velocities renders them susceptible to additive noise. This study proposes and evaluates a family of robust, Sobolev-regularized objective functions for the pairwise alignment of functional data, operating entirely within the original function space to avoid the need for numerical differentiation of the data. We define our optimization over a second-order Sobolev space and utilize the Centered Log-Ratio (CLR) transform to represent the warping functions. By penalizing both the velocity and acceleration of the centered log-derivative, this geometric approach preempts degenerate "pinching" artifacts and ensures the resulting warps are strictly monotonic, valid diffeomorphisms. In practice, this allows for highly efficient, unconstrained optimization within a finite-dimensional space. We systematically investigate four distinct pairwise data mismatch formulations: a Standard L2 baseline, a Symmetric L2 formulation, an Isometry (L2-preserving) mapping, and a Jacobian-weighted L2 functional. We establish robust theoretical foundations for these methods, proving the existence of optimal warps and the asymptotic consistency of the finite-dimensional estimators. Our results demonstrate that this CLR-regularized framework offers a powerful, computationally scalable, and noise-robust alternative to traditional derivative-based registration.