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What is PCA vs EOF: Variance Analysis Methods about?

PCA is used to reduce the dimensionality of large data sets while retaining most of the variation in the data. It works by transforming the data to a new coordinate system such that the greatest variance by any projection of the data lies on the first coordinate, called the first principal component. Subsequent components account for as much of the remaining variance as possible in descending order. The eigenvectors of the covariance or correlation matrix define the principal components, and the eigenvalues

Author

Muhammad Arief Rahman

Language

EN