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Okay so let me give you just a few more examples Michael of how PCA and ICA differ and I'm going to do this mainly by talking about certain things that I see a does and this is really just for your edification but I think it really helps to think about those sort of underlying models and how they differ by thinking about how they react differently to different kinds of data so just here's a couple of examples the one we covered right away was the blind source separation problem and of course we what we recall is that I say in some sense was designed to solve the blind source separation problem and in fact ICA does an excellent job at solving the blind source separation problem meanwhile PCA does a terrible job at solving the blind source separation problem and that's just because it's assuming these kind of Gaussian distribution of blind source route in it it's actually a directional and what I mean by that is you recall I drew this sort of matrix before where we had features this way and we had samples of those features like sound time samples of sound this way it turns out that for PCA it doesn't matter whether I give you this matrix or I give you the transpose of this matrix it ends up finding the same answer and that should make sense right because it's just basically finding a new rotation of the data and these are effectively just rotations of each other for the purposes of if you just kind of think about it geometrically in space I see on the other hand gives you completely different answers if you give it this versus giving it this so ICA is highly directional in PCA...