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| Titel |
Non-linear complex principal component analysis of nearshore bathymetry |
| VerfasserIn |
S. S. P. Rattan, B. G. Ruessink, W. W. Hsieh |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 12, no. 5 ; Nr. 12, no. 5 (2005-06-28), S.661-670 |
| Datensatznummer |
250010774
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| Publikation (Nr.) |
 copernicus.org/npg-12-661-2005.pdf |
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| Zusammenfassung |
| Complex principal component analysis (CPCA) is a useful linear
method for dimensionality reduction of data sets characterized by
propagating patterns, where the CPCA modes are linear functions of
the complex principal component (CPC), consisting of an amplitude
and a phase. The use of non-linear methods, such as the
neural-network based circular non-linear principal component
analysis (NLPCA.cir) and the recently developed non-linear complex
principal component analysis (NLCPCA), may provide a more accurate
description of data in case the lower-dimensional structure is
non-linear. NLPCA.cir extracts non-linear phase information
without amplitude variability, while NLCPCA is capable of
extracting both. NLCPCA can thus be viewed as a non-linear
generalization of CPCA. In this article, NLCPCA is applied to
bathymetry data from the sandy barred beaches at Egmond aan Zee
(Netherlands), the Hasaki coast (Japan) and Duck (North Carolina,
USA) to examine how effective this new method is in comparison to
CPCA and NLPCA.cir in representing propagating phenomena. At Duck,
the underlying low-dimensional data structure is found to have
linear phase and amplitude variability only and, accordingly, CPCA
performs as well as NLCPCA. At Egmond, the reduced data structure
contains non-linear spatial patterns (asymmetric bar/trough
shapes) without much temporal amplitude variability and,
consequently, is about equally well modelled by NLCPCA and
NLPCA.cir. Finally, at Hasaki, the data structure displays not
only non-linear spatial variability but also considerably temporal
amplitude variability, and NLCPCA outperforms both CPCA and
NLPCA.cir. Because it is difficult to know the structure of data
in advance as to which one of the three models should be used, the
generalized NLCPCA model can be used in each situation. |
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