query: Principal Components Analysis Terminology
positive_passages.docid: 17025
positive_passages.text: What exactly is called "principal component" in PCA?
negative_passages.docid: 126136
negative_passages.text: Fundamental question about dimensional analysis
negative_passages.docid: 86082
negative_passages.text: Interpreting PCA scores
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: 112134
negative_passages.text: What are principal component scores?
negative_passages.docid: 79786
negative_passages.text: How would PCA help with a k-means clustering analysis?
negative_passages.docid: 23640
negative_passages.text: Theory behind partial least squares regression
negative_passages.docid: 25410
negative_passages.text: What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)?
negative_passages.docid: 29381
negative_passages.text: How to perform dimensionality reduction with PCA in R
negative_passages.docid: 36609
negative_passages.text: Creating a single index from several principal components or factors retained from PCA/FA
negative_passages.docid: 25535
negative_passages.text: Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together
negative_passages.docid: 155987
negative_passages.text: Canonical Correlation analysis without raw data (algebra of CCA)
negative_passages.docid: 156944
negative_passages.text: Is there a test/technique/method for comparing principal components decompositions between samples?
negative_passages.docid: 143724
negative_passages.text: Is it possible to use kernel PCA for feature selection?
negative_passages.docid: 63042
negative_passages.text: What are the differences between Factor Analysis and Principal Component Analysis?
negative_passages.docid: 14181
negative_passages.text: Can averaging all the variables be seen as a crude form of PCA?
negative_passages.docid: 56670
negative_passages.text: The intuition behind generalized eigenvectors