By Da Li under guidance of Prof. Zhang Zhang.

Slow Feature Analysis(SFA)[1] is a method to learn invariant features in input signals. The purpose of the project is to implement Slow Feature Analysis(SFA) under Spark with large-scale training patches (more than 10 millions).

[1] Wiskott, L., Sejnowski, T.J.: Slow feature analysis: Unsupervised learning of invariances. Neural computation 14 (2002) 715-770.