Relevance Matrices in LVQ

Abstract. We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account. In comparison to the weighted euclidean metric used for GRLVQ, this metric is more powerful to represent the internal structure of the data appropriately while maintaining its excellent generalization ability as large margin optimizer. The algorithm is tested and compared to alternative LVQ schemes using an artificial dataset and the image segmentation data from the UCI repository.

• Type: Conference
• Year: 2007
• Where: DAGSTUHL
• Authors: Petra Schneider
• Keywords: DAGSTUHL 2007, Large Margin Optimizer, Matrix Learning Scheme, Relevance Learning Vector, Software Engineering