Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data.

ABSTRACT: It is quite common for functional data arising from imaging data to assume values in infinite-dimensional manifolds. Uncovering associations between two or more such nonlinear functional data extracted from the same object across medical imaging modalities can assist development of personalized treatment strategies. We propose a method for canonical correlation analysis between paired probability densities or shapes of closed planar curves, routinely used in biomedical studies, which combines a convenient linearization and dimension reduction of the data using tangent space coordinates. Leveraging the fact that the corresponding manifolds are submanifolds of unit Hilbert spheres, we describe how finite-dimensional representations of the functional data objects can be easily computed, which

SUBMITTER: Cho MH 

PROVIDER: S-EPMC9122284 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature