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?
What is the difference between Slow Feature Analysis (SFA) and a Moving Average?
machine learning;data mining;unsupervised learning