Multivariate Time-Series Analysis Via Manifold Learning

Pedro Luiz Coelho Rodrigues (1) , Marco Congedo (1) , Christian Jutten (1)

1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

This paper presents a data-driven approach for analyzing multivariate time series. It relies on the hypothesis that high-dimensional data often lie on a low-dimensional manifold whose geometry may be revealed using manifold learning techniques. We define a notion of distance between multi-variate time series and use it to determine a low-dimensional embedding capable of describing the statistics of the signals at hand using just a few parameters. We illustrate our method on two simulated examples and two real datasets containing electroencephalographic recordings (EEG).

Mots clés: Information Geometry; Manifold Learning; EEG Signal Analysis

Domaines: Traitement du signal et de l'image [eess.SP]; Machine Learning [stat.ML]

Pedro Luiz Coelho Rodrigues, Marco Congedo, Christian Jutten. Multivariate Time-Series Analysis Via Manifold Learning. SSP 2018 - 2018 IEEE Workshop on Statistical Signal Processing, Jun 2018, Fribourg-en-Brisgau, Germany. ⟨10.1109/SSP.2018.8450771⟩. ⟨hal-01868167⟩