Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams
Abstract
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.
Cite
@article{arxiv.1112.2113,
title = {Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams},
author = {Varun Raj Kompella and Matthew Luciw and Juergen Schmidhuber},
journal= {arXiv preprint arXiv:1112.2113},
year = {2012}
}