TY - JOUR
T1 - Nonlinear dimensionality reduction in climate data
AU - Gámez, A. J.
AU - ZHOU, Changsong
AU - Timmermann, A.
AU - Kurths, J.
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that makes the selection of a proper minimum number of subspaces for successfully representing the variability of the process ambiguous. The use of nonlinear methods can improve the embedding of multivariate data into lower dimensional manifolds. In this article, a nonlinear method for dimensionality reduction, Isomap, is applied to the sea surface temperature and thermocline data in the tropical Pacific Ocean, where the El Niño-Southern Oscillation (ENSO) phenomenon and the annual cycle phenomena interact. Isomap gives a more accurate description of the manifold dimensionality of the physical system. The knowledge of the minimum number of dimensions is expected to improve the development of low dimensional models for understanding and predicting ENSO.
AB - Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that makes the selection of a proper minimum number of subspaces for successfully representing the variability of the process ambiguous. The use of nonlinear methods can improve the embedding of multivariate data into lower dimensional manifolds. In this article, a nonlinear method for dimensionality reduction, Isomap, is applied to the sea surface temperature and thermocline data in the tropical Pacific Ocean, where the El Niño-Southern Oscillation (ENSO) phenomenon and the annual cycle phenomena interact. Isomap gives a more accurate description of the manifold dimensionality of the physical system. The knowledge of the minimum number of dimensions is expected to improve the development of low dimensional models for understanding and predicting ENSO.
UR - http://www.scopus.com/inward/record.url?scp=7044251937&partnerID=8YFLogxK
U2 - 10.5194/npg-11-393-2004
DO - 10.5194/npg-11-393-2004
M3 - Journal article
AN - SCOPUS:7044251937
SN - 1023-5809
VL - 11
SP - 393
EP - 398
JO - Nonlinear Processes in Geophysics
JF - Nonlinear Processes in Geophysics
IS - 3
ER -