Name
Title | ||
|---|---|---|
Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-Valued Data |
Abstract | ||
|---|---|---|
Performing large scale hypothesis testing on brain imaging data to identify group-wise differences (e.g., between healthy and diseased subjects) typically leads to a large number of tests (one per voxel). Multiple testing adjustment (or correction) is necessary to control false positives, which may lead to lower detection power in detecting true positives. Motivated by the use of socalled "independent filtering" techniques in statistics (for genomics applications), this paper investigates the use of independent filtering for manifold-valued data (e.g., Diffusion Tensor Imaging, Cauchy Deformation Tensors) which are broadly used in neuroimaging studies. Inspired by the concept of variance of a Riemannian Gaussian distribution, a type of non-specific data-dependent Riemannian variance filter is proposed. In practice, the filter will select a subset of the full set of voxels for performing the statistical test, leading to a more appropriate multiple testing correction. Our experiments on synthetic/simulated manifoldvalued data show that the detection power is improved when the statistical tests are performed on the voxel locations that "pass" the filter. Given the broadening scope of applications where manifold-valued data are utilized, the scheme can serve as a general feature selection scheme. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.99 | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
Riemannian Gaussian distribution,nonspecific data-dependent Riemannian variance filter,statistical test,synthetic manifold-valued data,voxel locations,feature selection,simulated manifold-valued data,neuroimaging studies,independent filtering,multiple testing adjustment,brain imaging data,large scale hypothesis testing,manifold-valued data | Voxel,Feature selection,Pattern recognition,Computer science,Filter (signal processing),Multiple comparisons problem,Cauchy distribution,Gaussian,Artificial intelligence,Statistical hypothesis testing,False positive paradox | Conference |
Volume | Issue | ISSN |
2017 | 1 | 2160-7508 |
ISBN | Citations | PageRank |
978-1-5386-0734-3 | 1 | 0.35 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ligang Zheng | 1 | 1 | 0.69 |
| Hyunwoo J. Kim | 2 | 41 | 8.17 |
| Nagesh Adluru | 3 | 208 | 20.57 |
| Michael A. Newton | 4 | 1 | 0.35 |
| Vikas Singh | 5 | 281 | 20.78 |