query: What exactly is a Principal component and Empirical Orthogonal Function?
positive_passages.docid: 17025
positive_passages.text: What exactly is called "principal component" in PCA?
negative_passages.docid: 17391
negative_passages.text: Diagonalisation and characteristic polynomial
negative_passages.docid: 41608
negative_passages.text: Weighted principal components analysis
negative_passages.docid: 14181
negative_passages.text: Can averaging all the variables be seen as a crude form of PCA?
negative_passages.docid: 59598
negative_passages.text: The vectors are orthonormal
negative_passages.docid: 113779
negative_passages.text: If $P$ is an orthogonal matrix its columns form an orthonormal set.
negative_passages.docid: 98317
negative_passages.text: What is the geometric relationship between the covariance matrix and the inverse of the covariance matrix?
negative_passages.docid: 12698
negative_passages.text: Orthonormal basis for $\mathcal{L}^2([0,1])$
negative_passages.docid: 7328
negative_passages.text: The proof of shrinking coefficients using ridge regression through "spectral decomposition"
negative_passages.docid: 117175
negative_passages.text: Proof that the 2-norm of orthogonal transformation of a matrix is invariant
negative_passages.docid: 6316
negative_passages.text: PCA on correlation or covariance?
negative_passages.docid: 25335
negative_passages.text: Orthogonal matrix over cross product
negative_passages.docid: 156166
negative_passages.text: PCA of non-Gaussian data
negative_passages.docid: 55662
negative_passages.text: How to use principal components analysis to select variables for regression?
negative_passages.docid: 10715
negative_passages.text: Orthogonal projection and two subspaces
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: 69474
negative_passages.text: Orthogonal projections with $\sum P_i =I$, proving that $i\ne j \Rightarrow  P_{j}P_{i}=0$