query: Principal Component Analysis PCA Terms and relationships: eigenvalues, eigenvectors, loadings, score matrix, and SVD
positive_passages.docid: 7603
positive_passages.text: Relationship between SVD and PCA. How to use SVD to perform PCA?
negative_passages.docid: 9286
negative_passages.text: Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal?
negative_passages.docid: 25535
negative_passages.text: Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together
negative_passages.docid: 14181
negative_passages.text: Can averaging all the variables be seen as a crude form of PCA?
negative_passages.docid: 39839
negative_passages.text: Doing principal component analysis or factor analysis on binary data
negative_passages.docid: 7328
negative_passages.text: The proof of shrinking coefficients using ridge regression through "spectral decomposition"
negative_passages.docid: 105175
negative_passages.text: Variable ordering using PCA
negative_passages.docid: 45858
negative_passages.text: How to compute varimax-rotated principal components in R?
negative_passages.docid: 103877
negative_passages.text: The advantages and disadvantages of using Kaiser Rule to select the number of principal components
negative_passages.docid: 85031
negative_passages.text: How to interpret this PCA biplot to determine which attributes to pick?
negative_passages.docid: 16641
negative_passages.text: Strange results of varimax rotation of principal component analysis in Stata: rotated components are all zeros and ones
negative_passages.docid: 16523
negative_passages.text: How to compute classification accuracy of PCA?
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: 102166
negative_passages.text: What is the relation between rank of a matrix, its eigenvalues and eigenvectors
negative_passages.docid: 155433
negative_passages.text: Examples of PCA where PCs with low variance are "useful"
negative_passages.docid: 81645
negative_passages.text: Combining PCA, feature scaling, and cross-validation without training-test data leakage
negative_passages.docid: 112134
negative_passages.text: What are principal component scores?