Singular-value decomposition and the Grassberger-Procaccia algorithm
- 1 September 1988
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review A
- Vol. 38 (6), 3017-3026
- https://doi.org/10.1103/physreva.38.3017
Abstract
A singular-value decomposition leads to a set of statistically independent variables which are used in the Grassberger-Procaccia algorithm to calculate the correlation dimension of an attractor from a scalar time series. This combination alleviates some of the difficulties associated with each technique when used alone, and can significantly reduce the computational cost of estimating correlation dimensions from a time series.Keywords
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