{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cho MH"],"funding":["NCI NIH HHS","National Institutes of Health","National Science Foundation","Engineering and Physical Sciences Research Council"],"pagination":["104870"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9122284"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["189"],"pubmed_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"],"journal":["Journal of multivariate analysis"],"pubmed_title":["Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data."],"pmcid":["PMC9122284"],"funding_grant_id":["R37 CA214955","EP/V048104/1","CCF-1839252","CCF-1740761","DMS-2015226","DMS-1613054","DMS-2015374","R37-CA214955"],"pubmed_authors":["Cho MH","Bharath K","Kurtek S"],"additional_accession":[]},"is_claimable":false,"name":"Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data.","description":"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","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-19T13:31:41.731Z","creation":"2025-04-19T13:31:41.731Z"},"accession":"S-EPMC9122284","cross_references":{"pubmed":["35601473"],"doi":["10.1016/j.jmva.2021.104870"]}}