Incremental Slow Feature Analysis
(IncSFA) is a biologically plausible,
covariance-free method to update
slow features, having
linear complexity with respect to input dimensionality
(batch SFA’s updating complexity is cubic).
IncSFA is amenable to processing of
high-dimensional visual input streams, useful for reinforcement learning,
and appropriate for developmental robots.
Slow feature representations enable a tractable
state-space for reinforcement learning on high-dimensional observations. Moreover, the incrementally-computed slow features
have formally been shown to be generally useful for RL, as they are
of proto-value functions. For example, see
the IncSFA-TD technique, which combines IncSFA and
temporal difference learning. And since we can compute the features incrementally, we can measure the features'
learning progress - applicable
to artificial curiosity, an important aspect of developmental robotics.
See on-going work on Curious Dr. MISFA.
Varun Raj Kompella, Matthew Luciw, and Juergen Schmidhuber (2011).
Incremental Slow Feature Analysis.
In Proceedings of the International Joint Conference on Artificial
Intelligence (IJCAI, Barcelona).