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arXiv · 1807.10629

Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series

Bastian Seifert·Katharina Korn·Steffen Hartmann·Christian Uhl
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Abstract

Multivariate signal processing is often based on dimensionality reduction techniques. We propose a new method, Dynamical Component Analysis (DyCA), leading to a classification of the underlying dynamics and - for a certain type of dynamics - to a signal subspace representing the dynamics of the data. In this paper the algorithm is derived leading to a generalized eigenvalue problem of correlation matrices. The application of the DyCA on high-dimensional chaotic signals is presented both for simulated data as well as real EEG data of epileptic seizures.

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Bastian Seifert, Katharina Korn, Steffen Hartmann, Christian Uhl. 2018-07-26. Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series. https://doi.org/10.1109/mlsp.2018.8517024

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