Theoretically and computationally convenient geometries on full-rank correlation matrices
About
In contrast to SPD matrices, few tools exist to perform Riemannian statistics on the open elliptope of full-rank correlation matrices. The quotient-affine metric was recently built as the quotient of the affine-invariant metric by the congruence action of positive diagonal matrices. The space of SPD matrices had always been thought of as a Riemannian homogeneous space. In contrast, we view in this work SPD matrices as a Lie group and the affine-invariant metric as a left-invariant metric. This unexpected new viewpoint allows us to generalize the construction of the quotient-affine metric and to show that the main Riemannian operations can be computed numerically. However, the uniqueness of the Riemannian logarithm or the Fr{\'e}chet mean are not ensured, which is bad for computing on the elliptope. Hence, we define three new families of Riemannian metrics on full-rank correlation matrices which provide Hadamard structures, including two flat. Thus the Riemannian logarithm and the Fr{\'e}chet mean are unique. We also define a nilpotent group structure for which the affine logarithm and the group mean are unique. We provide the main Riemannian/group operations of these four structures in closed form.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Classification | DataNAP HC vs. NAP | Accuracy69.9 | 6 | |
| Classification | DataPD HC vs. PD | Accuracy53.2 | 4 | |
| Brain Age Prediction | HCP-Aging | MAE (years)5.28 | 3 | |
| Brain Age Prediction | NKI | MAE6.38 | 3 | |
| Brain Age Prediction | cam-CAN | MAE (years)7.17 | 3 |