Title: Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening,
Abstract: This paper proposes Power Slow Feature Analysis, a gradient-based method to
extract temporally-slow features from a high-dimensional input stream that
varies on a faster time-scale, and a variant of Slow Feature Analysis (SFA).
While displaying performance comparable to hierarchical extensions to the SFA
algorithm, such as Hierarchical Slow Feature Analysis, for a small number of
output-features, our algorithm allows end-to-end training of arbitrary
differentiable approximators (e.g., deep neural networks). We provide
experimental evidence that PowerSFA is able to extract meaningful and
informative low-dimensional features in the case of a) synthetic
low-dimensional data, b) visual data, and also for c) a general dataset for
which symmetric non-temporal relations between points can be defined.