Robust growing hierarchical self organizing map

Sebastián Moreno, Héctor Allende, Cristian Rogel, Rodrigo Salas

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural network model that adapts its architecture during its unsupervised training process to represents the hierarchical relation of the data. However, the dynamical algorithm of the GHSOM is sensitive to the presence of noise and outliers, and the model will no longer preserve the topology of the data space as we will show in this paper. The outliers introduce an influence to the GHSOM model during the training process by locating prototypes far from the majority of data and generating maps for few samples data. Therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the GHSOM algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust GHSOM (RGHSOM). We will illustrate our technique on synthetic and real data sets.

Original languageEnglish
Pages (from-to)341-348
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2005
Externally publishedYes
Event8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain
Duration: 8 Jun 200510 Jun 2005


  • Artificial Neural Networks
  • Data Mining
  • Growing Hierarchical Self Organizing Maps
  • Robust Learning Algorithm


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