i can think of a few places where it's useful: if you know that your data comes from a stationary distribution, you can use it as a compression technique which

i can think of a few places where it's useful: if you know that your data comes from a stationary distribution, you can use it as a compression technique which reduces the computational demands on your model. sure, computing the initial svd or covariance matrix is expensive, but once you have it, the projection is just a matrix multiply and a vector subtraction. (with the reverse being the same) if you have some high dimensional data and you just want to look at it, it's a pretty good start. not only does it give you a sense for whether higher dimensions are just noise (by looking at the eigenspectrums) it also makes low dimensional plots possible. pca, cca and ica have been around for a very long time. i doubt "their time has passed." but who knows, maybe i'm wrong.