Adaptive relevance matrices in learning vector quantization

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features...

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Bibliographic Details
Published in:Neural computation Vol. 21; no. 12; p. 3532
Main Authors: Schneider, Petra, Biehl, Michael, Hammer, Barbara
Format: Journal Article
Language:English
Published: United States 01.12.2009
Subjects:
Algorithms
Artificial Intelligence
Automatic Data Processing
Brain - physiology
Humans
Information Theory
Learning - physiology
Probability Learning
ISSN:0899-7667
Online Access:Get more information
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Summary:We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. 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 and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C. elegans.
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ISSN:0899-7667
DOI:10.1162/neco.2009.11-08-908