A Geometric Approach to Pairwise Bayesian Alignment of Functional Data Using Importance Sampling

TL;DR

This paper introduces a Bayesian framework for nonlinear registration of functional data using Riemannian geometry and importance sampling, enabling efficient inference of multiple alignments and uncertainty quantification.

Contribution

It develops a geometric Bayesian model with importance sampling for pairwise functional data alignment, allowing for multimodal posterior exploration and uncertainty assessment.

Findings

Effective in identifying multiple plausible alignments.

Provides credible intervals for alignment uncertainty.

Validated on simulated and real biomedical data.

Abstract

We present a Bayesian model for pairwise nonlinear registration of functional data. We use the Riemannian geometry of the space of warping functions to define appropriate prior distributions and sample from the posterior using importance sampling. A simple square-root transformation is used to simplify the geometry of the space of warping functions, which allows for computation of sample statistics, such as the mean and median, and a fast implementation of a $k$-means clustering algorithm. These tools allow for efficient posterior inference, where multiple modes of the posterior distribution corresponding to multiple plausible alignments of the given functions are found. We also show pointwise $95\%$ credible intervals to assess the uncertainty of the alignment in different clusters. We validate this model using simulations and present multiple examples on real data from different application domains including biometrics and medicine.