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

@article{Kompella2011IncrementalSF, title={Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams}, author={Varun Raj Kompella and Matthew D. Luciw and J{\"u}rgen Schmidhuber}, journal={ArXiv}, year={2011}, volume={abs/1112.2113} }

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…

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