Growing neural gas efficiently

• Published in: Neurocomputing (Amsterdam) Vol. 104; pp. 72 - 82
• Main Authors: Fišer, Daniel, Faigl, Jan, Kulich, Miroslav
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.03.2013; Elsevier
• Subjects: 3-D surface reconstruction; Algorithms; Applied sciences; Artificial intelligence; Classification; Computer science; control theory; systems; Connectionism. Neural networks; Constraining; Data processing. List processing. Character string processing; Exact sciences and technology; Growing neural gas; Memory organisation. Data processing; Nearest neighbor search; Neural networks; Optimization; Pattern recognition. Digital image processing. Computational geometry; Preserves; Reconstruction; Self-organizing map; Software; Three dimensional; Nearest neighbor search; 3-D surface reconstruction; Growing neural gas; Self-organizing map; Cluster analysis; Nearest neighbour; Neural network; Kohonen algorithm; Optimization; Image reconstruction; Self organization; Classification; Internal structure; Tridimensional image; Reduced order systems; Large scale

This paper presents optimization techniques that substantially speed up the Growing Neural Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a subject of an intensive research interest in recent years as it is used in various practical applications. However, a p...