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{{StatsPsy}}
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In [[statistics]] and [[signal processing]], the method of '''empirical orthogonal function (EOF)''' analysis is a decomposition of a [[signal processing|signal]] or data set in terms of [[orthogonal]] [[basis function]]s which are determined from the data. It is the same as performing a [[principal components analysis]] on the data, except that the EOF method finds both [[time series]] and [[spatial]] patterns. The term is also interchangeble with the geographically weighted [[Principal components analysis|PCAs]] in [[geophysics]] <ref name=eofa>{{cite web
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| last = Stephenson
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| first = David
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| authorlink =
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| coauthors =
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| title = Empirical Orthogonal Function analysis
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| work =
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| publisher =
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| date =
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| url = http://www.uib.no/people/ngbnk/kurs/notes/node87.html
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| format =
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| doi =
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| accessdate = 20 September
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| accessyear = 2008 }}</ref>.
   
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The ''i''th basis function is chosen to be orthogonal to the basis functions from the first through ''i'' &minus; 1, and to minimize the residual [[variance]]. That is, the basis functions are chosen to be different from each other, and to account for as much variance as possible.
In [[statistics]] and [[signal processing]], the method of '''empirical orthogonal function (EOF)''' analysis is a decomposition of a [[signal processing|signal]] or data set in terms of [[orthogonal|orthogesal]] [[basis function]]s whtghe EOF method ftgsreinds both [[time series]] and [[spatial]] patterns. The term is also interchangeble withrgderygstr by computing the [[eigenvector|eitgsreectors]]tgrhe [[covariance matrix|covariancgt]] functions from the eigenvectors of the kernel mafgre thus nonsrfgsfr-linear in the locaesrn ogsdfgsf the data (see [[Mercer's theorem|Mercers]]
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Thus this method has much in common with the method of [[kriging]] in [[geostatistics]] and [[Gaussian process]] models.
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The method of EOF is similar in spirit to [[harmonic analysis]], but harmonic analysis typically uses predetermined orthogonal functions, for example, sine and cosine functions at fixed [[frequency|frequencies]]. In some cases the two methods may yield essentially the same results.
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The basis functions are typically found by computing the [[eigenvector]]s of the [[covariance matrix]] of the data set. A more advanced technique is to form a [[kernel (matrix)]] out of the data, using a fixed [[kernel (mathematics)|kernel]]. The basis functions from the eigenvectors of the kernel matrix are thus non-linear in the location of the data (see [[Mercer's theorem]] and the [[kernel trick]] for more information).
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==See also==
 
* [[Blind signal separation]]
 
* [[Blind signal separation]]
* [[Nonlinear dimensionality reduction|Nonlinesar dimensionality reesction]]
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* [[Nonlinear dimensionality reduction]]
 
* [[Orthogonal matrix]]
 
* [[Orthogonal matrix]]
* [[Varimax rotation|Source separatisergdsfg]]
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* [[Source separation]]
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* [[Transform coding]]
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* [[Varimax rotation]]
   
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== References & Notes ==
 
<div style="font-size:90%;">
 
<div style="font-size:90%;">
 
<references />
 
<references />
 
</div>
 
</div>
   
* Bjornsson sdfgs
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* Bjornsson Halldor and Silvia A. Venegas [http://www.vedur.is/~halldor/TEXT/eofsvd.html "A manual for EOF and SVD analyses of climate data"], McGill University, CCGCR Report No. 97-1, Montréal, Québec, 52pp., 1997.
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* David B. Stephenson and Rasmus E. Benestad. [http://www.gfi.uib.no/~nilsg/kurs/notes/ "Environmental statistics for climate researchers"]. ''(See: [http://www.gfi.uib.no/~nilsg/kurs/notes/node87.html "Empirical Orthogonal Function analysis"])''
   
 
* Christopher K. Wikle and Noel Cressie. "{{citeseer|A dimension reduced approach to space-time Kalman filtering|wikle99dimensionreduction}}", ''[[Biometrika]]'' 86:815-829, 1999.
 
* Christopher K. Wikle and Noel Cressie. "{{citeseer|A dimension reduced approach to space-time Kalman filtering|wikle99dimensionreduction}}", ''[[Biometrika]]'' 86:815-829, 1999.
   
{{enWP|Empirical orthogonal functions}}df
 
 
[[Category:Spatial data analysis]]
 
[[Category:Spatial data analysis]]
 
[[Category:Time series analysis]]
 
[[Category:Time series analysis]]
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{{enWP|Empirical orthogonal functions}}

Latest revision as of 16:06, September 19, 2010

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In statistics and signal processing, the method of empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the EOF method finds both time series and spatial patterns. The term is also interchangeble with the geographically weighted PCAs in geophysics [1].

The ith basis function is chosen to be orthogonal to the basis functions from the first through i − 1, and to minimize the residual variance. That is, the basis functions are chosen to be different from each other, and to account for as much variance as possible. Thus this method has much in common with the method of kriging in geostatistics and Gaussian process models.

The method of EOF is similar in spirit to harmonic analysis, but harmonic analysis typically uses predetermined orthogonal functions, for example, sine and cosine functions at fixed frequencies. In some cases the two methods may yield essentially the same results.

The basis functions are typically found by computing the eigenvectors of the covariance matrix of the data set. A more advanced technique is to form a kernel (matrix) out of the data, using a fixed kernel. The basis functions from the eigenvectors of the kernel matrix are thus non-linear in the location of the data (see Mercer's theorem and the kernel trick for more information).

See alsoEdit

References & Notes Edit

  1. Stephenson, David Empirical Orthogonal Function analysis. URL accessed on 20 September, 2008.
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