An extension to Slow Feature Analysis (xSFA)
Following our previous tutorial on Slow Feature Analysis (SFA) we now talk about xSFA - an unsupervised learning algorithm and extension to the original SFA algorithm that utilizes the slow features generated by SFA to reconstruct the individual sources of a nonlinear mixture, a process also known as Blind Source Separation (e.g. the reconstruction of individual voices from the recording of a conversation between multiple people). In this tutorial, we will provide a short example to demonstrate the capabilities of xSFA, discuss its limits, and offer some pointers on how and when to apply it. We also take a closer look at the theoretical background of xSFA to provide an intuition for the mathematics behind it.