query: What does it mean to compute eigenvectors of a covariance matrix if the data were not centered first?
positive_passages.docid: 7779
positive_passages.text: How does centering the data get rid of the intercept in regression and PCA?
negative_passages.docid: 90576
negative_passages.text: Symmetric matrix eigenvalues
negative_passages.docid: 3544
negative_passages.text: Eigenvectors of a circulant matrix
negative_passages.docid: 9250
negative_passages.text: Can the zero vector be an eigenvector for a matrix?
negative_passages.docid: 88820
negative_passages.text: Three linked question on non-negative definite matrices.
negative_passages.docid: 70571
negative_passages.text: Obtaining covariance matrix from correlation matrix
negative_passages.docid: 76477
negative_passages.text: Eigenvalues with transpose question.
negative_passages.docid: 67760
negative_passages.text: Should eigenvalues be ordered?
negative_passages.docid: 90089
negative_passages.text: Why are PCA eigenvectors orthogonal and what is the relation to the PCA scores being uncorrelated?
negative_passages.docid: 133568
negative_passages.text: Is it possible to have a basis for a covariance matrix such that the greatest variance is greater than the variance of the first eigenvector?
negative_passages.docid: 123573
negative_passages.text: Is a sample covariance matrix always symmetric and positive definite?
negative_passages.docid: 152395
negative_passages.text: Random matrix theory impact on covariance matrix analysis
negative_passages.docid: 3089
negative_passages.text: Why PCA of data by means of SVD of the data?
negative_passages.docid: 151088
negative_passages.text: Finding Matrix A from Eigenvalues and Eigenvectors (Diagonalization)
negative_passages.docid: 38169
negative_passages.text: Bounds on off-diagonal entries of a correlation matrix
negative_passages.docid: 42427
negative_passages.text: Understanding linear algebra in Ordinary Least Squares derivation
negative_passages.docid: 56670
negative_passages.text: The intuition behind generalized eigenvectors