Supervised neural gas with general similarity measure

Autor(en): Hammer, B
Strickert, M
Villmann, T
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; generalized relevance LVQ; learning vector quantization; metric adaptation; neural gas; SELF-ORGANIZING MAPS; VECTOR QUANTIZATION
Erscheinungsdatum: 2005
Herausgeber: SPRINGER
Journal: NEURAL PROCESSING LETTERS
Volumen: 21
Ausgabe: 1
Startseite: 21
Seitenende: 44
Zusammenfassung: 
Prototype based classification offers intuitive and sparse models with excellent generalization ability. However, these models usually crucially depend on the underlying Euclidian metric; moreover, online variants likely suffer from the problem of local optima. We here propose a generalization of learning vector quantization with three additional features: (I) it directly integrates neighborhood cooperation, hence is less affected by local optima; (II) the method can be combined with any differentiable similarity measure whereby metric parameters such as relevance factors of the input dimensions can automatically be adapted according to the given data; (III) it obeys a gradient dynamics hence shows very robust behavior, and the chosen objective is related to margin optimization.
ISSN: 13704621
DOI: 10.1007/s11063-004-3255-2

Show full item record

Google ScholarTM

Check

Altmetric