Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality
Joint Authors
Pires, Carlos A. L.
Hannachi, Abdel
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-23, 23 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-22
Country of Publication
Egypt
No. of Pages
23
Main Subjects
Abstract EN
We propose an expansion of multivariate time-series data into maximally independent source subspaces.
The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources.
We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis.
By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity.
Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors.
The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies.
Sources result essentially from the coherency of extremes of the data components.
The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies.
The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans.
Benefits and usefulness of independent subspaces are then discussed.
American Psychological Association (APA)
Pires, Carlos A. L.& Hannachi, Abdel. 2017. Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality. Complexity،Vol. 2017, no. 2017, pp.1-23.
https://search.emarefa.net/detail/BIM-1142680
Modern Language Association (MLA)
Pires, Carlos A. L.& Hannachi, Abdel. Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality. Complexity No. 2017 (2017), pp.1-23.
https://search.emarefa.net/detail/BIM-1142680
American Medical Association (AMA)
Pires, Carlos A. L.& Hannachi, Abdel. Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality. Complexity. 2017. Vol. 2017, no. 2017, pp.1-23.
https://search.emarefa.net/detail/BIM-1142680
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142680