query: PCA when # of features larger than # of samples?
positive_passages.docid: 24814
positive_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: 17765
negative_passages.text: How to use SVD for dimensionality reduction to reduce the number of columns (features) of the data matrix?
negative_passages.docid: 46319
negative_passages.text: How to perform orthogonal regression (total least squares) via PCA?
negative_passages.docid: 79786
negative_passages.text: How would PCA help with a k-means clustering analysis?
negative_passages.docid: 148679
negative_passages.text: Modelling with more variables than data points
negative_passages.docid: 125697
negative_passages.text: How to determine significant principal components using bootstrapping or Monte Carlo approach?
negative_passages.docid: 117085
negative_passages.text: Linear Discriminant Analysis for $p=1$
negative_passages.docid: 154304
negative_passages.text: Do PCA affect different classification methods?
negative_passages.docid: 114794
negative_passages.text: Is there any advantage of SVD over PCA?
negative_passages.docid: 70917
negative_passages.text: What's the difference between principal component analysis and multidimensional scaling?
negative_passages.docid: 25535
negative_passages.text: Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together
negative_passages.docid: 30255
negative_passages.text: Multiple regression with more than 20 predictors
negative_passages.docid: 81645
negative_passages.text: Combining PCA, feature scaling, and cross-validation without training-test data leakage
negative_passages.docid: 134201
negative_passages.text: How do I select the number of components for independent components analysis?
negative_passages.docid: 143724
negative_passages.text: Is it possible to use kernel PCA for feature selection?
negative_passages.docid: 86082
negative_passages.text: Interpreting PCA scores
negative_passages.docid: 87448
negative_passages.text: Why is a sample covariance matrix singular when sample size is less than number of variables?