Geometric foundations for scaling-rotation statistics on symmetric positive definite matrices: minimal smooth scaling-rotation curves in low dimensions
David Groisser, Sungkyu Jung, and Armin Schwartzman

TL;DR
This paper develops a geometric framework for analyzing symmetric positive definite matrices, especially in low dimensions, providing explicit formulas for minimal smooth scaling-rotation curves and distances, which are useful in applications like diffusion-tensor imaging.
Contribution
The paper introduces a systematic analysis of minimal smooth scaling-rotation curves on SPD matrices, with explicit formulas for low-dimensional cases p=2 and p=3, advancing geometric statistical analysis methods.
Findings
Explicit formulas for MSSR curves in p=2 and p=3 cases.
Closed-form expressions for scaling-rotation distance.
Identification of minimal curves in all nontrivial cases for p=3.
Abstract
We investigate a geometric computational framework, called the "scaling-rotation framework", on , the set of symmetric positive-definite (SPD) matrices. The purpose of our study is to lay geometric foundations for statistical analysis of SPD matrices, in situations in which eigenstructure is of fundamental importance, for example diffusion-tensor imaging (DTI). Eigen-decomposition, upon which the scaling-rotation framework is based, determines both a stratification of , defined by eigenvalue multiplicities, and fibers of the "eigen-composition" map . This leads to the notion of scaling-rotation distance [Jung et al. (2015)], a measure of the minimal amount of scaling and rotation needed to transform an SPD matrix, into another, by a smooth curve in . Our main goal in this…
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