Adaptive Feature Transformation for Image Data from Non-stationary Processes

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.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Erik Schaffernicht, Volker Stephan, Horst-Michael Gross
• Keywords: Artificial Intelligence, Common Transformation Methods, Feature Transformation Approach, ICANN 2009, Linear Discriminant Analysis