Welllll it was specific to tICA, and I was trying to make it more general. Maybe it's not possible

I suppose that it will work with anything in which there are eigenvector and eigenvalues that produce the reduced dimension, with the eigenvalues' magnitude used for picking the which vectors to project onto

So this would encompass quite a few methods, like tICA, diff. maps. ISOMAP, PCA, etc. but I suppose there may be some cases where it doesn't work (like low-rank matrix factorization). 

What do you think is preferable? Should I have a tICADistance class? Eventually there will be a ktICADistance (though maybe not in msmbuilder) and others too