Adaptive Feature Transformation for Image Data from Non-stationary Processes

  • Authors:

  • Erik Schaffernicht;Volker Stephan;Horst-Michael Gross

  • Affiliations:

  • Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, Germany 98693;Powitec Intelligent Technologies GmbH, Essen-Kettwig, Germany 45219;Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, Germany 98693

  • Venue:
  • ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
  • Year:
  • 2009

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Abstract

This paper introduces the application of the feature transformation approach proposed by Torkkola [1] to the domain of image processing. Thereto, we extended the approach and identifed its advantages and limitations. We compare the results with more common transformation methods like Principal Component Analysis and Linear Discriminant Analysis for a function approximation task from the challenging domain of video-based combustion optimization. It is demonstrated that the proposed method generates superior results in very low dimensional subspaces. Further, we investigate the usefulness of an adaptive variant of the introduced method in comparison to basic subspace transformations and discuss the results.