Domain Adaptation Using Riemannian Geometry of Spd Matrices

Gal Maman, Or Yair, Danny Eytan, Ronen Talmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Pages4464-4468
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Riemannian manifolds
  • high-dimensional signal analysis
  • parallel transport
  • transfer learning

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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