Incremental Slow Feature Analysis

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 approximations 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.

Hierarchical SFA, combined with competitive learning, has been shown to learn place cells, head-direction cells, and grid cells, from high-dimensional visual input. We developed a Hierarchical IncSFA. Results show that the top layer IncSFA units indeed had learned to code for position, and the responses of several competitively learned units operate as place cells.

Check out the WCCI 2014 Tutorial: Slow Feature Analysis for Curiosity-Driven Agents!


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