Learning vector quantization for multimodal data

Autor(en): Hammer, B
Strickert, M
Villmann, T
Herausgeber: Dorronsoro, JR
Stichwörter: ALGORITHM; Computer Science; Computer Science, Artificial Intelligence
Erscheinungsdatum: 2002
Herausgeber: SPRINGER-VERLAG BERLIN
Journal: ARTIFICIAL NEURAL NETWORKS - ICANN 2002
LECTURE NOTES IN COMPUTER SCIENCE
Volumen: 2415
Startseite: 370
Seitenende: 376
Zusammenfassung: 
Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype-based clustering algorithm. Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions. As iterative algorithms with local learning rules, LVQ and modifications crucially depend on the initialization of the prototypes. They often fail for multimodal data. W,e propose a variant of GRLVQ which introduces ideas of the neural gas algorithm incorporating a global neighborhood coordination of the prototypes. The resulting learning algorithm, supervised relevance neural gas, is capable of learning highly multimodal data, whereby it shares the benefits of a gradient dynamics and an adaptive metric with GRLVQ.
Beschreibung: 
12th International Conference on Artifical Neural Networks (ICANN 2002), MADRID, SPAIN, AUG 28-30, 2002
ISBN: 9783540440741
ISSN: 03029743

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