期刊
NEURAL NETWORKS
卷 56, 期 -, 页码 35-48出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.05.001
关键词
Self-organizing map topologies; Tessellations; Clustering; Image segmentation; Classification
资金
- Ministry of Economy and Competitiveness of Spain [TIN2011-24141]
- Autonomous Government of Andalusia (Spain) [TIC-6213, TIC-657]
- European Regional Development Fund (ERDF)
- Ministry of Science and Innovation of Spain [RYC-2010-05663]
The original Self-Organizing Feature Map (SOFM) has been extended in many ways to suit different goals and application domains. However, the topologies of the map lattice that we can found in literature are nearly always square or, more rarely, hexagonal. In this paper we study alternative grid topologies, which are derived from the geometrical theory of tessellations. Experimental results are presented for unsupervised clustering, color image segmentation and classification tasks, which show that the differences among the topologies are statistically significant in most cases, and that the optimal topology depends on the problem at hand. A theoretical interpretation of these results is also developed. (C) 2014 Elsevier Ltd. All rights reserved.
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