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Growing neural gas efficiently
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DOI：10.1016/j.neucom.2012.10.004.png)
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Abstract
En 中文
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. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Self-organizing map
Growing neural gas
Nearest neighbor search
3-D surface reconstruction
Journal
IF:
6.5
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2.5W
Citations:
6.5W