Principal Component Analysis
Principal component analysis (PCA) performs a linear transformation
of the coordinate system, so as to maximize the variance of the data
along the first principal axis of the new coordinate system.
More information on Wikipedia.
Components Range:
You can choose the number of dimensions after
projection that you keep (this might be useful to reduce the
dimensionnality of the dataset for further processing)
Check the
Components Range box and set the desired dimensions.
Cumulated variance and eigenvalues
The eigenvalues of each eigenvector and the cumulated variance
explained by the first dimensions.
Recontruction error