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