Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities

M. Kaden, D. Nebel, F. Melchert, A. Backhaus, U. Seiffert, T. Villmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this paper we propose a rank measure for comparison of (dis-)similarities regarding their behavior to reflect data dependencies. It is based on evaluation of dissimilarity ranks, which reflects the topological structure of the data in dependence of the dissimilarity measure. The introduced rank measure can be used to select dissimilarity measures in advance before cluster or classification learning algorithms are applied. Thus time consuming learning of models with different dissimilarities can be avoided.

Original languageEnglish
Title of host publication12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings
EditorsJean-Charles Lamirel, Madalina Olteanu, Marie Cottrell
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages8
ISBN (Electronic)9781509066384
DOIs
Publication statusPublished - 31-Aug-2017
Event12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Nancy, France
Duration: 28-Jun-201730-Jun-2017

Publication series

Name12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings

Conference

Conference12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017
CountryFrance
CityNancy
Period28/06/201730/06/2017

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