@inproceedings{8e79067ae70f49aba69083d126300069,
title = "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.",
keywords = "Riemannian manifolds, high-dimensional signal analysis, parallel transport, transfer learning",
author = "Gal Maman and Or Yair and Danny Eytan and Ronen Talmon",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "https://doi.org/10.1109/ICASSP.2019.8682989",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "4464--4468",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
}