Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images.

ABSTRACT: Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (x_i , y_i ) pairs, are Euclidean is well studied. The setting where y_i is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, x_i ∈ R, against a manifold-valued variable, y_i ∈ . We seek to substantially extend the

SUBMITTER: Kim HJ 

PROVIDER: S-EPMC4288036 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature