Output details
11 - Computer Science and Informatics
University of Brighton
Article title
Non-Euclidian statistics for covariance matrices, with applications to diffusion tensor imaging
Type
D - Journal article
Title of journal
Annals of Applied Statistics
Article number
0
Volume number
3
Issue number
3
First page of article
1102
ISSN of journal
1932-6157
Year of publication
2009
Number of additional authors
2
Additional information
<28>
This paper is the first to define non-Euclidean statistics for covariance matrix data. The non-Euclidean methods proposed in this paper have a significant contribution to analyzing advanced medical images in the form of diffusion tensor images. This work has been built on by many researchers especially in covariance matrix data analysis, for example, by Aston, Huckemann and Pennec. Aston has also applied this method to analyze phonetic covariance structure in philological studies.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
21
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
