When looking at the eigenvectors of the covariance matrix, we get the directions of maximum variance (the first eigenvector is the direction in which the data varies the most, etc.); this is called principal component analysis (PCA).

I was wondering what it would mean to look at the eigenvectors/values of the mutual information matrix, would they point in the direction of maximum entropy?