Domain Adaptation Using Riemannian Geometry of Spd Matrices

Abstract

In this paper, we propose a new unsupervised domain adaptation method based on the Riemannian geometry of Symmetric Positive-Definite (SPD) matrices. The proposed domain adaptation is based on parallel transport (PT) and moments alignment. We show that this method facilitates meaningful comparisons between data points from different domains, while preserving the inherent internal structure of each domain. Experimental results demonstrate the adaptation of high-dimensional noisy electrophysiological signals collected from different subjects.