Perform PCA. Extract PCs. Can one then tell what the most important _original_ features were, from the PCs?
26181
Using principal component analysis (PCA) for feature selection
45832
What is the relation between k-means clustering and PCA?
36477
Is there any required amount of variance captured by PCA in order to do later analyses?
7779
How does centering the data get rid of the intercept in regression and PCA?
21651
Estimating the most important features in a k-means cluster partition
66335
Accuracy of shape area measurements when GCS shapefile is source of layer in PCS Data Frame?
134201
How do I select the number of components for independent components analysis?
114794
Is there any advantage of SVD over PCA?
91776
Understanding which features were most important for logistic regression
42297
Minimum sample size for PCA or FA when the main goal is to estimate only few components?
55662
How to use principal components analysis to select variables for regression?
3089
Why PCA of data by means of SVD of the data?
51254
How to project a new vector onto the PC space using kernel PCA?
146238
How to deal with a data-set having all the samples with every feature-point almost similar?
7632
Interpretation of biplots in principal components analysis
10466
Making sense of principal component analysis, eigenvectors & eigenvalues
79763
What do empty (missing) values in the output of PCA or factor analysis mean in R?