Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-Valued Data

Ligang Zheng, Hyunwoo J. Kim, Nagesh Adluru, Michael A. Newton, Vikas Singh; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 20-29

Abstract

Performing large scale hypothesis testing on brain imaging data to identify group-wise differences (e.g., between healty 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 so-called "independent filtering" techniques in statistics (for genomics applications), this paper investigates the use of independent filtering for manifold-valued data (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

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[bibtex]
@InProceedings{Zheng_2017_CVPR_Workshops,
author = {Zheng, Ligang and Kim, Hyunwoo J. and Adluru, Nagesh and Newton, Michael A. and Singh, Vikas},
title = {Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-Valued Data},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}