query: Is PCA effectively just a rotation of the data, if you were to keep all principal components?
positive_passages.docid: 143908
positive_passages.text: What are "rotated" and "unrotated" principal components, given that PCA always rotates the coordinates axes?
negative_passages.docid: 118997
negative_passages.text: What are the four axes on PCA biplot?
negative_passages.docid: 55662
negative_passages.text: How to use principal components analysis to select variables for regression?
negative_passages.docid: 155987
negative_passages.text: Canonical Correlation analysis without raw data (algebra of CCA)
negative_passages.docid: 3589
negative_passages.text: How to reverse PCA and reconstruct original variables from several principal components?
negative_passages.docid: 39112
negative_passages.text: Does a correlation matrix of two variables always have the same eigenvectors?
negative_passages.docid: 126726
negative_passages.text: PCA, LDA, CCA, and PLS
negative_passages.docid: 81049
negative_passages.text: What norm of the reconstruction error is minimized by the low-rank approximation matrix obtained with PCA?
negative_passages.docid: 85031
negative_passages.text: How to interpret this PCA biplot to determine which attributes to pick?
negative_passages.docid: 25535
negative_passages.text: Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together
negative_passages.docid: 126136
negative_passages.text: Fundamental question about dimensional analysis
negative_passages.docid: 154899
negative_passages.text: Is it valid to perform PCA if Kaiser-Meyer-Olkin (KMO) index is very low?
negative_passages.docid: 111321
negative_passages.text: When should we discretize/bin continuous independent variables/features and when should not?
negative_passages.docid: 14465
negative_passages.text: "Normalizing" variables for SVD / PCA
negative_passages.docid: 139690
negative_passages.text: Which variables should I transform, center, and/or standardize in my data for Principal Component Analysis?
negative_passages.docid: 158823
negative_passages.text: Principal component analysis "backwards": how much variance of the data is explained by a given linear combination of the variables?
negative_passages.docid: 6590
negative_passages.text: PCA and proportion of variance explained