query: Reference for this claim: important features in data can be "hidden" in the higher PCA axes that are typically thrown out
positive_passages.docid: 155433
positive_passages.text: Examples of PCA where PCs with low variance are "useful"
negative_passages.docid: 36477
negative_passages.text: Is there any required amount of variance captured by PCA in order to do later analyses?
negative_passages.docid: 22754
negative_passages.text: "matrix is not positive definite" - even when highly correlated variables are removed
negative_passages.docid: 5843
negative_passages.text: Why do people rule out local hidden variables?
negative_passages.docid: 125697
negative_passages.text: How to determine significant principal components using bootstrapping or Monte Carlo approach?
negative_passages.docid: 88611
negative_passages.text: How are Random Forests not sensitive to outliers?
negative_passages.docid: 45832
negative_passages.text: What is the relation between k-means clustering and PCA?
negative_passages.docid: 139690
negative_passages.text: Which variables should I transform, center, and/or standardize in my data for Principal Component Analysis?
negative_passages.docid: 67557
negative_passages.text: Imputation of missing values for PCA
negative_passages.docid: 70245
negative_passages.text: Why is the eigenvector in PCA taken to be unit norm?
negative_passages.docid: 26181
negative_passages.text: Using principal component analysis (PCA) for feature selection
negative_passages.docid: 20763
negative_passages.text: Why could centering independent variables change the main effects with moderation?
negative_passages.docid: 13787
negative_passages.text: Why log-transforming the data before performing principal component analysis?
negative_passages.docid: 89791
negative_passages.text: Correlated features produce strange weights in Logistic Regression
negative_passages.docid: 7632
negative_passages.text: Interpretation of biplots in principal components analysis
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: 14181
negative_passages.text: Can averaging all the variables be seen as a crude form of PCA?