  In this paper, we consider the problem of domain adaptation. We propose to
view the data through the lens of covariance matrices and present a method for
domain adaptation using parallel transport on the cone manifold of symmetric
positive-definite matrices. We provide rigorous analysis using Riemanninan
geometry, illuminating the theoretical guarantees and benefits of the presented
method. In addition, we demonstrate these benefits using experimental results
on simulations and real-measured data.
