Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data

Authors:
M. Strickert;J. Keilwagen;F. -M. Schleif;T. Villmann;M. Biehl
Affiliations:
Research Group Data Inspection, IPK Gatersleben, Germany;Research Group Data Inspection, IPK Gatersleben, Germany;Research Group Computational Intelligence, University of Leipzig, Germany;Dept. of Computer Science, University of Applied Sciences Mittweida, Germany;Intelligent Systems Group, University of Groningen, The Netherlands
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

A structurally simple, yet powerful, formalism is presented for adapting attribute combinations in high-dimensional data, given categorical data class labels. The rank-1 Mahalanobis distance is optimized in a way that maximizes between-class variability while minimizing within-class variability. This optimization target has resemblance to Fisher's linear discriminant analysis (LDA), but the proposed formulation is more general and yields improved class separation, which is demonstrated for spectrum data and gene expression data.