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Results tagged with pca
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user 31277
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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PCA on standardized variables: how do eigenvectors relate to original, unstandardized variab...
PCA performs a linear transformation on a data set to obtain a new data set, this time with eigenvector basis and eigenvalue loadings. … I've searched through 23/34 pages on PCA, and was not able to find an answer to this question. If its out there, my apologies, I tried. …
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Non-Orthogonality in PCA?
i) PCA only looks for orthogonal components because it is computationally easiest. … If your data set is not orthogonal, such as the ferris wheel example, then PCA would not be able to find these solutions. …
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