Principal component analysis
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original
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Characteristic vector analysisConditional principal components analysisEigenimageEigenvector analysisHotelling transformKL transformKarhunen–Loève transformNIPALSNonlinear iterative partial least squaresPrincipal-component analysisPrincipal-components analysisPrincipal Component AnalysisPrincipal Components AnalysisPrincipal componentPrincipal component analysis/version 2Principal componentsPrincipal components analysisPrinciple Component AnalysisPrinciple component analysisPrinciple componentsPrinciple components analysisProbabilistic principal component analysisProper Orthogonal DecompositionProper orthogonal decomposition
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Principal component analysis
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original
has abstract
A Análise de Componentes Princ ...... ma matriz levemente diferente.
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Analiza głównych składowych (a ...... zmiennych nie są porównywalne.
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Die Hauptkomponentenanalyse (d ...... Surfaces oder die Kernel-PCA.
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En estadística, el análisis de ...... ara la extracción de factores.
@es
Hoofdcomponentenanalyse, of pr ...... correspondentieanalyse (CCA).
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L'analisi in componenti princi ...... i latenti (feature reduction).
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L'analyse en composantes princ ...... e l'inertie ou de la variance.
@fr
Principal component analysis ( ...... variance in a single dataset.
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Метод главных компонент (англ. ...... а (англ. Hotelling transform).
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تحليل العنصر الرئيسي أو تحليل ...... اتج عن اختزال الأبعاد الأصلية.
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title
Stanford University video by Andrew Ng
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A Análise de Componentes Princ ...... anteriores. Os componentes pri
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Analiza głównych składowych (a ...... yjaśniają początkowe czynniki.
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Die Hauptkomponentenanalyse (d ...... erschied der beiden Verfahren
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En estadística, el análisis de ...... madas componentes principales.
@es
Hoofdcomponentenanalyse, of pr ...... rd van de factoren te bepalen.
@nl
L'analisi in componenti princi ...... rtiene all'analisi fattoriale.
@it
L'analyse en composantes princ ...... 'information moins redondante.
@fr
Principal component analysis ( ...... lative scaling of the original
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Метод главных компонент (англ. ...... данных, в общественных науках.
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تحليل العنصر الرئيسي أو تحليل ...... اتج عن اختزال الأبعاد الأصلية.
@ar
label
Analisi delle componenti principali
@it
Analiza głównych składowych
@pl
Analyse en composantes principales
@fr
Análise de componentes principais
@pt
Análisis de componentes principales
@es
Hauptkomponentenanalyse
@de
Hoofdcomponentenanalyse
@nl
Principal component analysis
@en
Метод главных компонент
@ru
تحليل العنصر الرئيسي
@ar