I have started to read more about Slow Feature Analysis and I was wondering how SFA differed from simply taking a moving average?
The linked article suggests, "SFA is an unsupervised algorithm that learns (nonlinear) functions that extract slowly-varying signals from their input data.".
I understand that it is certainly more complicated than just taking a moving average on a signal, but I don't really understand what the benefit or purpose is of the learned non-linear functions.
What would be some typical applications of SFA?