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No Neuron Left Behind: A genetic approach to higher precision topological mapping of self-organizing maps

Chris Gorman
  • University of Massachusetts Dartmouth, 285 Old Westport Road, 02747 North Dartmouth MA, United States of America
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Clint Rogers
  • University of Massachusetts Dartmouth, 285 Old Westport Road, 02747 North Dartmouth MA, United States of America
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Iren Valova
  • University of Massachusetts Dartmouth, 285 Old Westport Road, 02747 North Dartmouth MA, United States of America
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-04-01 | DOI: https://doi.org/10.1515/comp-2015-0001


Self-organizing maps are extremely useful in the field of pattern recognition. They become less useful, however, when neurons fail to activate during training. This phenomenon occurs when neurons are initialized in areas of non-input and are far enough away from the input data to never move toward the input. These neurons effectively misrepresent the data set. This results in, among other things, patterns becoming unrecognizable.We introduce an algorithm called No Neuron Left Behind to solve this problem.We show that our algorithm produces a more accurate topological representation of the input space.We also show that no neuron clusters form in areas of noninput and that mapping quality of the SOM increases drastically when our algorithm is implemented. Finally, the running time of NNLB is better or comparable to classic SOM without it.

Keywords: self-organizing maps; artificial neural networks; genetic algorithms; optimization


  • [1] T. Kohonen, Self-organizing maps, Springer, Berlin, New York, 2001 Google Scholar

  • [2] T. Yamagutchi et al., Pattern Recognition of EEG Signal during Motor Imagery by Using SOM, In Innovative Computing, Information and Control, 2007. ICICIC’07, 121–124, 2007 Google Scholar

  • [3] Y. Ke-ming et al., Clustering analysis on disease severity of wheat stripe rust based on som neural network, Natural Computation (ICNC) 1, 421–425, 2011 Google Scholar

  • [4] M. Tomita, H. Matsushita, Y. Nishio, Behavior of fatigable som and its application to clustering, Neural Netw. 2006, IJCNN ’06, 3526–3531 Google Scholar

  • [5] M. Cordina, C. Debono, Increasing wireless sensor network lifetime through the application of som neu- ral networks, In Communications, Control and Signal Processing, 2008, ISCCSP 2008, 467–471, 2008 Google Scholar

  • [6] D. Kit, Y. Kong, Y. Fu, Location aware self-organizing map for discovering similar and unique visual features of geographical locations, In International Joint Conference on Neural Netw. (IJCNN), Beijing, China, 2014 Google Scholar

  • [7] T. Rumbell, S. Denham, T. Wennekers, A spiking self-organizing map combining stdp, oscillations, and continuous learning, IEEE Trans. Neural Netw. Learn. Syst. 25(5), 894–907, 2014 Web of ScienceCrossrefGoogle Scholar

  • [8] P. Vecer, M. Kreidl, R. Smid, Application of the self-organizing map to manual automotive transmission, In Signal Processing and Information Technology, 2003, ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on. 2003, 612–615 Google Scholar

  • [9] X. Qiang, G. Cheng, Z. Wang, An overview of some classical growing neural networks and new developments, Education Technology and Computer (ICETC) 3, V3–351–V3–355, 2010 Google Scholar

  • [10] I. Valova, D. Beaton, A. Buer, D. MacLean, Fractal initialization for high-qualitymapping with self-organizingmaps, Neural Comput. Appl. 953–966, 2010 CrossrefGoogle Scholar

  • [11] A.K. Qin, P.N. Suganthan, Robust growing neural gas algorithm with application in cluster analysis, Neural Netw. 17(8), 1135– 1148, 2004 Google Scholar

  • [12] M. Dittenbach, D. Merkl„ A. Rauber, The growing hierarchical self-organizing map. In Neural Netw., IEEE-INNS-ENNS International Joint Conference on. IEEE Computer Society 6, IV–15 –IV– 19, 2000 Google Scholar

  • [13] B. Fritzke, A growing neural gas network learns topologies, In: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds), NIPS. MIT Press, Cambridge, Massachusetts, USA, 1994, 625–632 Google Scholar

  • [14] S. Furao, T. Ogura, O. Hasegawa, An enhanced self-organizing incremental neural network for online unsupervised learning, Neural Netw. 20(8), 893–903, 2007 Web of ScienceGoogle Scholar

  • [15] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, Gsa: a gravitational search algorithm, Inform. Sciences 179(13), 2232–2248, 2009 Google Scholar

  • [16] S. Yazdani, H. Nezamabadi-pour, S. Kamyab, A gravitational search algorithm for multimodal optimization, Swarm Evol. Comput. 14, 1–14, 2014 Google Scholar

  • [17] C. Fourie, W. Perold, Comparison of genetic algorithms to other optimization techniques for raising circuit yield in superconducting digital circuits, IEEE Trans. Appl. Supercond. 13(2), 511– 514, 2003 CrossrefGoogle Scholar

  • [18] R.J. Kuo, C.M. Chao, Y. Chiu, Application of particle swarm optimization to association rule mining, Appl. Soft Comput. 11(1), 326–336, 2011 CrossrefGoogle Scholar

  • [19] S. Mallick, S. Ghoshal, P. Acharjee, S. Thakur, Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm, Int. J. Elec. Power 52, 254– 265, 2013 Web of ScienceCrossrefGoogle Scholar

  • [20] K. Sarath, V. Ravi, Association rule mining using binary particle swarm optimization, Eng. Appl. Artif. Intel. 26(8), 1832–1840, 2013 Web of ScienceCrossrefGoogle Scholar

  • [21] X. Han, L. Quan, X. Xiong, B. Wu, Facing the classification of binary problemswith a hybrid system based on quantum-inspired binary gravitational search algorithm and k-nn method, Eng. Appl. Artif. Intel. 26(10), 2424–2430, 2013 Web of ScienceCrossrefGoogle Scholar

  • [22] D. Beaton, I. Valova, D.MacLean, Cqoco: A measure for comparative quality of coverage and organization for self-organizing maps, Neurocomput. 73(10-12), 2147–2159, 2010 CrossrefGoogle Scholar

  • [23] D. Polani, Organization measures for self-organizing maps, In Helsinki University of Technology, 1997, 280–285 Google Scholar

  • [24] M.M. Van Hulle, Self-organizing maps, Handbook of Natural Computing: Theory, Experiments, and Applications, Springer- Verlag, Berlin, Germany, 1–45, 2010 Google Scholar

  • [25] T. Villmann, R. Der, M. Herrmann, T.Martinetz, Topology preservation in self-organizing feature maps: exact definition and measurement, IEEE Trans. Neural Netw. 8(2), 256–266, 1997 CrossrefGoogle Scholar

  • [26] M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, Cambridge, Massachusetts, USA, 1996 Google Scholar

  • [27] A. Barolli, F. Xhafa, C. Sanchez, M. Takizawa, A study on the effect of mutation in genetic algorithms for mesh router placement problem in wireless mesh networks, CISIS 2011, 32–39, 2011 Google Scholar

  • [28] B. Moon, H. Jagadish, C. Faloutsos, J. Saltz, Analysis of the clustering properties of the hilbert space-filling curve, IEEE Trans. Knowl. Data Eng. 13(1), 124–141, 2001 CrossrefGoogle Scholar

  • [29] D. Beaton, I. Valova, D. MacLean, Color objects identification with TurSOM, ICCNS 2009, International Conference on Cognitive and Neural Systems, Boston, Massachusetts, USA, 2009 Google Scholar

  • [30] D. Beaton, I. Valova, D. MacLean, Growing mechanisms and cluster identification with TurSOM, IJCNN 2009, International Joint Conference on Neural Networks, Atlanta, Georgia, USA, 2009 Google Scholar

  • [31] D. Beaton, I. Valova, D.MacLean, TurSOM: A turing inspired selforganizing map, In Neural Netw. 2009, IJCNN 2009, 288–295, 2009 Google Scholar

About the article

Received: 2013-07-18

Accepted: 2014-12-22

Published Online: 2015-04-01

Citation Information: Open Computer Science, Volume 5, Issue 1, ISSN (Online) 2299-1093, DOI: https://doi.org/10.1515/comp-2015-0001.

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©2015 C. Gorman et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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