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Cited by 460

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Singular-value decomposition and the Grassberger-Procaccia algorithm

Albano, A. M., Muench, J., Schwartz, C., Mees, A. I., Rapp, P. E.·1988·Physical Review A

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.

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Metadata

Authors
Albano, A. M., Muench, J., Schwartz, C., Mees, A. I., & Rapp, P. E.
Venue
Physical Review A
Published
1988-09-01
Volume
38
Issue
6
Pages
3017-3026
ISSN
0556-2791 (ISSN-L)
OpenAlex
W2033410765
Cited by
460
References
3
FWCI
17.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Singular-value decomposition and the Grassberger-Procaccia algorithm. https://app.cochat.ai/papers/10.1103/physreva.38.3017
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Albano, A. M., Muench, J., Schwartz, C., Mees, A. I., & Rapp, P. E. (1988). Singular-value decomposition and the Grassberger-Procaccia algorithm. Physical Review A. https://doi.org/10.1103/physreva.38.3017

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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