Name
Title | ||
|---|---|---|
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 yi 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) is an element of R, against a manifold-valued variable, y(i) is an element of M. We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression - a manifold-valued dependent variable against multiple independent variables, i. e., f : R-n is an element of M. Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.352 | CVPR |
Keywords | Field | DocType |
vectors,shape analysis,computational modeling,linear regression,learning artificial intelligence,regression analysis,statistical analysis,hilbert spaces,shape,principal components analysis,manifolds,image classification,principal component analysis,odf,medical imaging,public domain software,image resolution,topic modeling | Generalized linear model,Variables,Artificial intelligence,Manifold,Linear regression,Mathematical optimization,Parametric model,Pattern recognition,Pure mathematics,Parametric statistics,Geodesic,Mathematics,Unit sphere | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1063-6919 |
Citations | PageRank | References |
12 | 0.64 | 13 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyunwoo J. Kim | 1 | 41 | 8.17 |
| Barbara B. Bendlin | 2 | 20 | 1.51 |
| Nagesh Adluru | 3 | 208 | 20.57 |
| Maxwell Collins | 4 | 40 | 3.96 |
| Moo K. Chung | 5 | 707 | 60.36 |
| Sterling Johnson | 6 | 494 | 44.59 |
| Richard J. Davidson | 7 | 478 | 50.39 |
| Vikas Singh | 8 | 562 | 49.01 |