Visualizing High Dimensional Dynamical Processes

Andrés F. Duque; Guy Wolf; Kevin R. Moon

doi:10.1109/mlsp.2019.8918875

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arXiv · 1906.10725

Visualizing High Dimensional Dynamical Processes

Andrés F. Duque·Guy Wolf·Kevin R. Moon

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Abstract

Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years. While these methods are effective at learning a low-dimensional representation, they are often insufficient for visualizing the global and local structure of the data. In this paper, we present DIG (Dynamical Information Geometry), a visualization method for multivariate time series data that extracts an information geometry from a diffusion framework. Specifically, we implement a novel group of distances in the context of diffusion operators, which may be useful to reveal structure in the data that may not be accessible by the commonly used diffusion distances. Finally, we present a case study applying our visualization tool to EEG data to visualize sleep stages.

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BibTeX RIS

Andrés F. Duque, Guy Wolf, Kevin R. Moon. 2019-06-25. Visualizing High Dimensional Dynamical Processes. https://doi.org/10.1109/mlsp.2019.8918875

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