  Slow Feature Analysis (SFA) extracts features representing the underlying
causes of changes within a temporally coherent high-dimensional raw sensory
input signal. Our novel incremental version of SFA (IncSFA) combines
incremental Principal Components Analysis and Minor Components Analysis. Unlike
standard batch-based SFA, IncSFA adapts along with non-stationary environments,
is amenable to episodic training, is not corrupted by outliers, and is
covariance-free. These properties make IncSFA a generally useful unsupervised
preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA
and MCA updates take the form of Hebbian and anti-Hebbian updating, extending
the biological plausibility of SFA. In both single node and deep network
versions, IncSFA learns to encode its input streams (such as high-dimensional
video) by informative slow features representing meaningful abstract
environmental properties. It can handle cases where batch SFA fails.
