Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

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

 
: Kaden, Marika; Nebel, David; Melchert, Friedrich; Backhaus, Andreas; Seiffert, Udo; Villmann, Thomas

:
Postprint urn:nbn:de:0011-n-4737844 (2.0 MByte PDF)
MD5 Fingerprint: dfd59efe7f6cc8c81c5cc8a2708d3292
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 11.9.2018


Lamirel, J.-C. ; Institute of Electrical and Electronics Engineers -IEEE-:
12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM+ 2017 : Nancy, France, June 28-30, 2017. Proceedings
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-6638-4 (online)
ISBN: 978-1-5090-6639-1 (print)
S.220-226
International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+) <12, 2017, Nancy>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IFF ()

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.

: http://publica.fraunhofer.de/dokumente/N-473784.html