Slow Feature Analysis
Slow feature analysis (SFA) is an unsupervised learning algorithm for extracting slowly varying features from a multidimensional input signal in time. It is not based on low-pass filtering, i.e., temporal averaging, but combines input components of single time steps into temporally stable features. SFA can be used for nonlinear dimensionality reduction and learning of invariant representations. Algorithmically, it is closely related to principal component analysis (PCA).
The slowness principle is based on the observation that different representations of a sensorial input vary on different time scales. For instance, a zebra grazing in the savanna is a scene that changes slowly. This scene is represented in the eyes of an observer in terms of activities of retinal receptors, which, due to the black-and-white stripes of the zebra, change quickly between high and low values whenever the zebra moves or the gaze of the observer changes....
- Dähne S, Wilbert N, Wiskott L (2014) Slow feature analysis on retinal waves leads to VI complex cells. PLoS Comput Biol, in pressGoogle Scholar
- Escalante-B AN, Wiskott L (2012) Slow feature analysis: perspectives for technical applications of a versatile learning algorithm. Künstliche Intell [Artif Intell] 26(4):341–348Google Scholar
- Wiskott L, Berkes P, Franzius M, Sprekeler H, Wilbert N (2011) Slow feature analysis. Scholarpedia 6(4):5282Google Scholar