A brief introduction to Slow Feature Analysis – Hacker Noon

SFA is an unsupervised learning method to extract the smoothest (slowest) underlying functions or features from a time series. This can be used for dimensionality reduction, regression and classification. For example, we can have a highly erratic series that is determined by a nicer behaving latent variable. Let's start by generating time series D and S: This is known as the logistic map. By plotting the series S, we can inspect its chaotic nature.