Slow Feature Analysis (SFA)



Slow Feature Analysis (SFA) is a technique invented by Laurenz Wiskott which finds unsupervisedly features in complex timeseries.

The guidance principle of SFA is to find such combinations of the original inputs which vary slowly in time. The application areas of SFA range from visual pattern recognition over general classification up to 

classVarianceunsupervised driving force detection in time series.

Within the project SFA @ CIOP (as part of the SOMA project), we have currently the following achievements:

  • We used SFA for the first time for gesture recognition [Koch10a] and achieved results comparable to other state-of-the-art gesture classifiers.
  • In order to do so, we expanded the original open-source, MATLAB-based SFA-Toolkit (sfa-tk V1.0) by Pietro Berkes to a new version sfa-tk V2.6 which is available for download. The extensions of V2.6 are:  more robust SVD-based SFA (see [Kon09b] for details), support for classification, support for cross validation, Gaussian classifier, some classification demos and sample data (UCI, gesture).
  • If the number of training samples is small, the original SFA classification algorithm will fail. We extended the SFA classification algorithm of Pietro Berkes by a parametric bootstrap algorithm which makes SFA classification applicable for small numbers of examples. This extension is available in sfa-tk V2.8 for download.
  • In a driving-force experiment we showed that SFA can detect signals slower than the driving force and that SFA may ‘switch concepts’ in a certain way which has some parallels to human perception [Kon09a][KonK10a][KonK10b].
  • In 2012 a group of students around Martin Zaefferer successfully ported sfa-tk V2.8 to R: The R-package rSFA is  available for download on CRAN. This allows to use SFA for unsupervised feature generation in data mining tasks and to integrate it as a general preprocessing tool in the SOMA project and the TDMR framework.

In the near future, we plan to pursue these further goals with SFA @ CIOP:

  • Improve gesture classification for the difficult task of recognizing gestures of unseen persons and towards even fewer training examples to learn new gestures.
  • Currently students are working on porting the MATLAB-based SFA-toolkit and the gesture recognition engine to the iPhone environment to make the gesture recognition available as iPhone App.


Wolfgang Konen, Kristine Hein, Patrick Koch


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