Slow Feature Analysis Toolkit for Matlab

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Dear Comp-neuros,

I am pleased to announce the release of sfa-tk, a Matlab
implementation of the slow feature analysis algorithm.

Slow feature analysis (SFA) is an unsupervised algorithm that learns
(nonlinear) functions that extract slowly-varying signals from their
input data. The learned functions tend to be invariant to frequent
transformations of the input and the extracted slowly-varying signals
can be interpreted as generative sources of the observed input data.
These properties make SFA suitable for many data processing
applications and as a model for sensory processing in the brain
(http://itb.biologie.hu-berlin.de/~berkes/slowness/project_description.shtml).
SFA is a one-shot algorithm, and it is guaranteed to find the optimal
solution (within the considered function space) in a single step.

For a detailed description see Wiskott, L. and Sejnowski,
T.J. (2002). Slow Feature Analysis: Unsupervised Learning of
Invariances. Neural Computation, 14(4):715-770. or refer
to
http://itb.biologie.hu-berlin.de/~wiskott/Projects/LearningInvariances.html

sfa-tk has been designed to handle long and relatively high
dimensional data sets. It is possible to define arbitrary function
spaces. The package and online documentation can be found at

http://itb.biologie.hu-berlin.de/~berkes/software/sfa-tk/sfa-tk.shtml

Best regards,

Pietro Berkes.

--------------------------------------------------------------
Pietro Berkes
Institute for Theoretical Biology
Humboldt University Berlin
http://itb.biologie.hu-berlin.de/~berkes/
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