query: PCA on data having more dimensions than observations
positive_passages.docid: 18487
positive_passages.text: Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
negative_passages.docid: 118103
negative_passages.text: Does PCA's reconstruction error get reduced with more PCs being used?
negative_passages.docid: 93145
negative_passages.text: Can I add several additional variables to my principal component regression?
negative_passages.docid: 153028
negative_passages.text: Showing that the product of vector magnitudes is larger than their dot product
negative_passages.docid: 36609
negative_passages.text: Creating a single index from several principal components or factors retained from PCA/FA
negative_passages.docid: 154304
negative_passages.text: Do PCA affect different classification methods?
negative_passages.docid: 6590
negative_passages.text: PCA and proportion of variance explained
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: 22551
negative_passages.text: What can cause PCA to worsen results of a classifier?
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: 113346
negative_passages.text: What is the proper association measure of a variable with a PCA component (on a biplot / loading plot)?
negative_passages.docid: 24814
negative_passages.text: Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
negative_passages.docid: 55662
negative_passages.text: How to use principal components analysis to select variables for regression?
negative_passages.docid: 6206
negative_passages.text: How does centering make a difference in PCA (for SVD and eigen decomposition)?
negative_passages.docid: 70245
negative_passages.text: Why is the eigenvector in PCA taken to be unit norm?
negative_passages.docid: 14465
negative_passages.text: "Normalizing" variables for SVD / PCA
negative_passages.docid: 17025
negative_passages.text: What exactly is called "principal component" in PCA?