Konferenzpaper

Jahr der Veröffentlichung: 2002

Seiten: 370-376

Zeitschrift: Lecture notes in computer science

Bandnummer: 2415

ISSN: 0302-9743

ISBN: 3-540-44074-7

DOI Link: https://doi.org/10.1007/3-540-46084-5_60

Konferenz: 12th International Conference on Artifical Neural Networks (ICANN 2002)

Verlag: Springer

Abstract: 
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.

Harvard-Zitierstil: Hammer, B., Strickert, M. and Villmann, T. (2002) Learning vector quantization for multimodal data, Lecture notes in computer science, 2415, pp. 370-376. https://doi.org/10.1007/3-540-46084-5_60

APA-Zitierstil: Hammer, B., Strickert, M., & Villmann, T. (2002). Learning vector quantization for multimodal data. Lecture notes in computer science. 2415, 370-376. https://doi.org/10.1007/3-540-46084-5_60