Robustness analysis of the neural gas learning algorithm

Carolina Saavedra, Sebastián Moreno, Rodrigo Salas, Héctor Allende

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

2 Scopus citations


The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data, The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540465561, 9783540465560
StatePublished - 2006
Externally publishedYes
Event11th Iberoamerican Congress in Pattern Recognition, CIARP 2006 - Cancun, Mexico
Duration: 14 Nov 200617 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4225 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Iberoamerican Congress in Pattern Recognition, CIARP 2006


  • M-estimators
  • Neural gas
  • Robust learning algorithm


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