query: PCA demeaning the data
positive_passages.docid: 7779
positive_passages.text: How does centering the data get rid of the intercept in regression and PCA?
negative_passages.docid: 156166
negative_passages.text: PCA of non-Gaussian data
negative_passages.docid: 67557
negative_passages.text: Imputation of missing values for PCA
negative_passages.docid: 152242
negative_passages.text: How can I map data to lower dimension?
negative_passages.docid: 89237
negative_passages.text: PCA: Eigenvectors of opposite sign and not being able to compute eigenvectors with `solve` in R
negative_passages.docid: 70917
negative_passages.text: What's the difference between principal component analysis and multidimensional scaling?
negative_passages.docid: 70245
negative_passages.text: Why is the eigenvector in PCA taken to be unit norm?
negative_passages.docid: 39839
negative_passages.text: Doing principal component analysis or factor analysis on binary data
negative_passages.docid: 6590
negative_passages.text: PCA and proportion of variance explained
negative_passages.docid: 85751
negative_passages.text: Trouble with imputed data set
negative_passages.docid: 48240
negative_passages.text: Iterative process for removing extreme samples
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: 14381
negative_passages.text: I'm getting "jumpy" loadings in rollapply PCA in R. Can I fix it?
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: 154757
negative_passages.text: One component in PCA is always the mean vector in two-dimensions but not three
negative_passages.docid: 3089
negative_passages.text: Why PCA of data by means of SVD of the data?
negative_passages.docid: 97435
negative_passages.text: Reversing PCA back to the original variables