Growing neural gas efficiently

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...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 104; pp. 72 - 82
Main Authors Fišer, Daniel, Faigl, Jan, Kulich, Miroslav
Format Journal Article
LanguageEnglish
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
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Summary: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 poor time performance on large scale problems requiring neural networks with a high amount of nodes can be a limiting factor for further applications (e.g., cluster analysis, classification, 3-D reconstruction) or a wider usage. We propose two optimization techniques that are aimed exclusively on an efficient implementation of the GNG algorithm internal structure rather than on a modification of the original algorithm. The proposed optimizations preserve all properties of the GNG algorithm and enable to use it on large scale problems with reduced computational requirements in several orders of magnitude.
Bibliography:ObjectType-Article-1
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.10.004