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Slow Feature Analysis (SFA) extracts features representing the underlyingcauses of changes within a temporally coherent high-dimensional raw sensoryinput signal. Our novel incremental version of SFA (IncSFA) combinesincremental Principal Components Analysis and Minor Components Analysis. Unlikestandard batch-based SFA, IncSFA adapts along with non-stationary environments,is amenable to episodic training, is not corrupted by outliers, and iscovariance-free. These properties make IncSFA a generally useful unsupervisedpreprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCAand MCA updates take the form of Hebbian and anti-Hebbian updating, extendingthe biological plausibility of SFA. In both single node and deep networkversions, IncSFA learns to encode its input streams (such as high-dimensionalvideo) by informative slow features representing meaningful abstractenvironmental properties. It can handle cases where batch SFA fails.

Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams