Robustness analysis of the neural gas learning algorithm

Carolina Saavedra, Sebastián Moreno, Rodrigo Salas, Héctor Allende

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
EditorialSpringer Verlag
Páginas559-568
Número de páginas10
ISBN (versión impresa)3540465561, 9783540465560
DOI
EstadoPublicada - 2006
Publicado de forma externa
Evento11th Iberoamerican Congress in Pattern Recognition, CIARP 2006 - Cancun, México
Duración: 14 nov. 200617 nov. 2006

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen4225 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia11th Iberoamerican Congress in Pattern Recognition, CIARP 2006
País/TerritorioMéxico
CiudadCancun
Período14/11/0617/11/06

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