Multivariate Time-Series Analysis Via Manifold Learning

Pedro Luiz Coelho Rodrigues 1 Marco Congedo 1 Christian Jutten 1
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
Abstract : 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).
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Pedro Luiz Coelho Rodrigues, Marco Congedo, Christian Jutten. Multivariate Time-Series Analysis Via Manifold Learning. IEEE Statistical Signal Processing Workshop (SSP 2018), Jun 2018, Fribourg-en-Brisgau, Germany. ⟨10.1109/SSP.2018.8450771⟩. ⟨hal-01868167⟩

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