Informational Rescaling of PCA Maps with Application to Genetics

Nassim Nicholas Taleb∗, Pierre Zalloua, and Dan Platt
∗Corresponding author, nnt1@nyu.edu Dec 2019

We discuss the inadequacy of covariances/correlations and other measures in L-2 as relative distance metrics. We propose a computationally simple heuristic to transform a map based on standard principal component analysis (PCA) (when the variables are asymptotically Gaussian) into an entropy-based map where distances are based on mutual information.

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