Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series
Signal Processing
2019-03-19 v2 Machine Learning
Chaotic Dynamics
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.
Cite
@article{arxiv.1807.10629,
title = {Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series},
author = {Bastian Seifert and Katharina Korn and Steffen Hartmann and Christian Uhl},
journal= {arXiv preprint arXiv:1807.10629},
year = {2019}
}
Comments
Published in Proc. 2018 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING; 7 figures; Corrected formula (16)