Longitudinal Canonical Correlation Analysis
Applications
2023-02-03 v1 Methodology
Abstract
This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.
Keywords
Cite
@article{arxiv.2302.00841,
title = {Longitudinal Canonical Correlation Analysis},
author = {Seonjoo Lee and Jongwoo Choi and Zhiqian Fang and F. DuBois Bowman},
journal= {arXiv preprint arXiv:2302.00841},
year = {2023}
}
Comments
24 pages, 16 figures