DagSemProc.07131.7.pdf
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Relevance Matrices in LVQ
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Dagstuhl Seminar Proceedings, Volume 7131
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2007-09-12
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Petra Schneider. Relevance Matrices in LVQ. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)
https://doi.org/10.4230/DagSemProc.07131.7
https://doi.org/10.4230/DagSemProc.07131.7
Abstract
LVQ-networks belong to the class of distance-based classifiers. The underlying distance measure is of special importance for their performance, because it defines how the data items are compared and how they are grouped in clusters.
Relevance Learning techniques try to adapt the distance measure to the specific data used for training. I will present a new adaptive distance measure in Learning Vector Quantization which is an extension of previously proposed Relevance Learning schemes. In comparison to the already existing techniques for Relevance Learning, this distance measure is more powerful to represent the internal structure of the data appropriately.
Two applications will be used to demonstrate the behavior of the new algorithm (artificial and real life).
Keywords
- Learning Vector Quantization
- Relevance Learning
- adaptive distance measure
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