"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. The term is also interchangeable with the geographically weighted Principal components analysis in geophysics. The i th basis function is chosen to be orthogonal to the basis functions from the first through i \u2212 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."@en . . . . . . . "\u7D4C\u9A13\u7684\u76F4\u4EA4\u95A2\u6570"@ja . . . "\u7D4C\u9A13\u7684\u76F4\u4EA4\u95A2\u6570\uFF08\u3051\u3044\u3051\u3093\u3066\u304D\u3061\u3087\u3063\u3053\u3046\u304B\u3093\u3059\u3046\u3001Empirical Orthogonal Function\uFF09\u3068\u306F\u591A\u5909\u91CF\u89E3\u6790\u306E\u624B\u6CD5\u306E1\u3064\u3067\u3042\u308B\u4E3B\u6210\u5206\u5206\u6790\u3092\u7528\u3044\u3066\u8A08\u7B97\u3055\u308C\u305F\u4E3B\u6210\u5206\u30D9\u30AF\u30C8\u30EB\u306E\u3053\u3068\u3002\u8907\u6570\u306E\u5909\u6570\u540C\u58EB\u306E\u5171\u5206\u6563\uFF08\u307E\u305F\u306F\u76F8\u95A2\uFF09\u3092\u8A08\u7B97\u3057\u3066\u5171\u5206\u6563\u884C\u5217\uFF08\u307E\u305F\u306F\u76F8\u95A2\u884C\u5217\uFF09\u3092\u4F5C\u6210\u3057\u3001\u305D\u306E\u6700\u3082\u5927\u304D\u306A\u56FA\u6709\u5024\u306B\u5BFE\u5FDC\u3059\u308B\u56FA\u6709\u30D9\u30AF\u30C8\u30EB\u3092\u7B2C\u4E00\u4E3B\u6210\u5206\uFF08EOF-1\uFF09\u3068\u3059\u308B\u3002\u5171\u5206\u6563\u884C\u5217\u306E\u56FA\u6709\u5024\u306F\u5206\u6563\u306E\u5927\u304D\u3055\u3092\u8868\u3059\u306E\u3067\u3001EOF-1\u306F\u6700\u3082\u5206\u6563\u3092\u5927\u304D\u304F\u3059\u308B\u3088\u3046\u306A\u6210\u5206\u3068\u306A\u308B\u3002\u7B2C\u4E8C\u4E3B\u6210\u5206\uFF08EOF-2\uFF09\u306F\u3001EOF-1\u306B\u76F4\u4EA4\u3059\u308B\u6210\u5206\u306E\u3046\u3061\u6700\u3082\u5206\u6563\u3092\u5927\u304D\u304F\u3059\u308B\u3088\u3046\u306A\uFF08EOF-1\u306B\u5BFE\u5FDC\u3059\u308B\u56FA\u6709\u5024\u3092\u9664\u3051\u3070\u6700\u3082\u56FA\u6709\u5024\u306E\u5927\u304D\u306A\uFF09\u56FA\u6709\u30D9\u30AF\u30C8\u30EB\u3067\u3042\u308B\u3002"@ja . . . . "Empirical orthogonal functions"@en . "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. The term is also interchangeable with the geographically weighted Principal components analysis in geophysics. The i th basis function is chosen to be orthogonal to the basis functions from the first through i \u2212 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. The method of EOF analysis 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 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)."@en . . . . "En statistique et traitement du signal, la m\u00E9thode d\u00E9composition orthogonale aux valeurs propres consiste \u00E0 d\u00E9composer des donn\u00E9es avec des fonctions orthogonales d\u00E9termin\u00E9es \u00E0 partir des donn\u00E9es (en anglais : empirical orthogonal functions, abr\u00E9g\u00E9 en EOF). C'est la m\u00EAme chose que de faire une analyse en composante principale except\u00E9 que les EOF permettent d'obtenir \u00E0 la fois des formes (patterns) temporelles et spatiales. Les EOF sont aussi appel\u00E9s PCA en g\u00E9ophysique. Pour faire simple : les EOF permettent de synth\u00E9tiser l'information pour faciliter l'analyse."@fr . . . "1087385357"^^ . . "3353"^^ . . . . . . "D\u00E9composition orthogonale aux valeurs propres"@fr . . . . . . "429618"^^ . . "\u7D4C\u9A13\u7684\u76F4\u4EA4\u95A2\u6570\uFF08\u3051\u3044\u3051\u3093\u3066\u304D\u3061\u3087\u3063\u3053\u3046\u304B\u3093\u3059\u3046\u3001Empirical Orthogonal Function\uFF09\u3068\u306F\u591A\u5909\u91CF\u89E3\u6790\u306E\u624B\u6CD5\u306E1\u3064\u3067\u3042\u308B\u4E3B\u6210\u5206\u5206\u6790\u3092\u7528\u3044\u3066\u8A08\u7B97\u3055\u308C\u305F\u4E3B\u6210\u5206\u30D9\u30AF\u30C8\u30EB\u306E\u3053\u3068\u3002\u8907\u6570\u306E\u5909\u6570\u540C\u58EB\u306E\u5171\u5206\u6563\uFF08\u307E\u305F\u306F\u76F8\u95A2\uFF09\u3092\u8A08\u7B97\u3057\u3066\u5171\u5206\u6563\u884C\u5217\uFF08\u307E\u305F\u306F\u76F8\u95A2\u884C\u5217\uFF09\u3092\u4F5C\u6210\u3057\u3001\u305D\u306E\u6700\u3082\u5927\u304D\u306A\u56FA\u6709\u5024\u306B\u5BFE\u5FDC\u3059\u308B\u56FA\u6709\u30D9\u30AF\u30C8\u30EB\u3092\u7B2C\u4E00\u4E3B\u6210\u5206\uFF08EOF-1\uFF09\u3068\u3059\u308B\u3002\u5171\u5206\u6563\u884C\u5217\u306E\u56FA\u6709\u5024\u306F\u5206\u6563\u306E\u5927\u304D\u3055\u3092\u8868\u3059\u306E\u3067\u3001EOF-1\u306F\u6700\u3082\u5206\u6563\u3092\u5927\u304D\u304F\u3059\u308B\u3088\u3046\u306A\u6210\u5206\u3068\u306A\u308B\u3002\u7B2C\u4E8C\u4E3B\u6210\u5206\uFF08EOF-2\uFF09\u306F\u3001EOF-1\u306B\u76F4\u4EA4\u3059\u308B\u6210\u5206\u306E\u3046\u3061\u6700\u3082\u5206\u6563\u3092\u5927\u304D\u304F\u3059\u308B\u3088\u3046\u306A\uFF08EOF-1\u306B\u5BFE\u5FDC\u3059\u308B\u56FA\u6709\u5024\u3092\u9664\u3051\u3070\u6700\u3082\u56FA\u6709\u5024\u306E\u5927\u304D\u306A\uFF09\u56FA\u6709\u30D9\u30AF\u30C8\u30EB\u3067\u3042\u308B\u3002"@ja . . . "En statistique et traitement du signal, la m\u00E9thode d\u00E9composition orthogonale aux valeurs propres consiste \u00E0 d\u00E9composer des donn\u00E9es avec des fonctions orthogonales d\u00E9termin\u00E9es \u00E0 partir des donn\u00E9es (en anglais : empirical orthogonal functions, abr\u00E9g\u00E9 en EOF). C'est la m\u00EAme chose que de faire une analyse en composante principale except\u00E9 que les EOF permettent d'obtenir \u00E0 la fois des formes (patterns) temporelles et spatiales. Les EOF sont aussi appel\u00E9s PCA en g\u00E9ophysique. Pour faire simple : les EOF permettent de synth\u00E9tiser l'information pour faciliter l'analyse."@fr . . . . . . . . . . . . . . . . .