Singular-value decomposition and the Grassberger-Procaccia algorithm

A. M. Albano; J. Muench; C. Schwartz; A. I. Mees; P. E. Rapp

doi:10.1103/PhysRevA.38.3017

Singular-value decomposition and the Grassberger-Procaccia algorithm

A. M. Albano^*, J. Muench, C. Schwartz, A. I. Mees, P. E. Rapp

^*Corresponding author for this work

Military and Emergency Medicine

Research output: Contribution to journal › Article › peer-review

449 Scopus citations

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.

Original language	English
Pages (from-to)	3017-3026
Number of pages	10
Journal	Physical Review A
Volume	38
Issue number	6
DOIs	https://doi.org/10.1103/PhysRevA.38.3017
State	Published - 1988

Access to Document

10.1103/PhysRevA.38.3017

Cite this

@article{2753b848d3344a69bf6ebdf5b0ae1907,

title = "Singular-value decomposition and the Grassberger-Procaccia algorithm",

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.",

author = "Albano, \{A. M.\} and J. Muench and C. Schwartz and Mees, \{A. I.\} and Rapp, \{P. E.\}",

year = "1988",

doi = "10.1103/PhysRevA.38.3017",

language = "English",

volume = "38",

pages = "3017--3026",

journal = "Physical Review A",

issn = "1050-2947",

publisher = "American Physical Society",

number = "6",

}

TY - JOUR

T1 - Singular-value decomposition and the Grassberger-Procaccia algorithm

AU - Albano, A. M.

AU - Muench, J.

AU - Schwartz, C.

AU - Mees, A. I.

AU - Rapp, P. E.

PY - 1988

Y1 - 1988

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=35949011725&partnerID=8YFLogxK

U2 - 10.1103/PhysRevA.38.3017

DO - 10.1103/PhysRevA.38.3017

M3 - Article

AN - SCOPUS:35949011725

SN - 1050-2947

VL - 38

SP - 3017

EP - 3026

JO - Physical Review A

JF - Physical Review A

IS - 6

ER -

Singular-value decomposition and the Grassberger-Procaccia algorithm

Abstract

Access to Document

Fingerprint

Cite this