Pattern Recognition with Sparse Coding

  • Thomas Martinetz (Institut für Neuro- und Bioinformatik, Universität Lübeck)
A3 01 (Sophus-Lie room)


Technical automation often requires to solve pattern recognition problems. For pattern recognition one needs features which clearly describe the patterns involved. As soon as such features are identified, also the following classification task becomes feasible. Unfortunately, there is no theoretically founded method yet for extracting features with which a pattern recognition problem can be solved optimally. The determination of good features is nowadays based on heuristics and experience of the expert. To take a look at nature might be helpful. The visual cortex employs so called "sparse coding" for representing natural images. We show that on images of technical applications this "sparse coding" generates features with which superior pattern recognition performance can be achieved. This is demonstrated with experiments on digit recognition and face finding. The results support the hypotheses that with "sparse coding" nature eventually has found a universal mechanism for generating good features, and that this mechanism can be highly advantageous also in technical applications.