query: SVD for PCA: Why would one standardize the data matrix?
positive_passages.docid: 6316
positive_passages.text: PCA on correlation or covariance?
negative_passages.docid: 21071
negative_passages.text: How to perform SVD to impute missing values, a concrete example
negative_passages.docid: 135428
negative_passages.text: Weird correlations in the SVD results of random data; do they have a mathematical explanation or is it a LAPACK bug?
negative_passages.docid: 156166
negative_passages.text: PCA of non-Gaussian data
negative_passages.docid: 98829
negative_passages.text: How to convert the matrix completion problem to the standard SDP form?
negative_passages.docid: 7603
negative_passages.text: Relationship between SVD and PCA. How to use SVD to perform PCA?
negative_passages.docid: 37653
negative_passages.text: Singular covariance matrix in Mahalanobis distance in Matlab
negative_passages.docid: 116362
negative_passages.text: Is PCA unstable under multicollinearity?
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: 97435
negative_passages.text: Reversing PCA back to the original variables
negative_passages.docid: 51254
negative_passages.text: How to project a new vector onto the PC space using kernel PCA?
negative_passages.docid: 10466
negative_passages.text: Making sense of principal component analysis, eigenvectors & eigenvalues
negative_passages.docid: 7328
negative_passages.text: The proof of shrinking coefficients using ridge regression through "spectral decomposition"
negative_passages.docid: 42447
negative_passages.text: SVD: How to find the columnvector of U corresponding to a singular value equal to zero
negative_passages.docid: 154304
negative_passages.text: Do PCA affect different classification methods?
negative_passages.docid: 150043
negative_passages.text: How do I use the SVD in collaborative filtering?
negative_passages.docid: 86082
negative_passages.text: Interpreting PCA scores