[D] What are some effective dimensionality reduction (unsupervised feature selection) techniques for a high dimensional, sparse dataset?

I am considering comparing mutual information scores, but I also don't think I understand MI well enough.

For example, I(X;Y) = H(X) + H(Y) - H(X,Y). To me, visualizing H(X) and H(Y) as venn diagrams and H(X,Y) as the information from both X, Y (like an overlapping venn diagram) makes me think that when X, Y are disjoint, then MI is 0 and when X, Y overlap completely, then the MI score will be high. So, I'm thinking that a …

dataset diagrams dimensionality example feature feature selection information machinelearning the information think unsupervised