A quick assessment of topology preservation for SOM structures

IEEE Transactions on Neural Networks

Volumn 18, Issue 5, 2007, Pages 1524-1528

  Vidaurre, Diego ^a   Muruzábal, Jorge ^a  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; MONITORING; TOPOGRAPHY; TOPOLOGY;

TOPOGRAPHIC MAP FORMATION ALGORITHMS; TOPOLOGY PRESERVATION;

SELF ORGANIZING MAPS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; LETTER; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 34548640457     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.895820     Document Type: Article

References (16)

1. T. Kohonen, Self-Organizing Maps, 3rd ed. Berlin, Germany: Springer-Verlag, 2001.
2. H.-U. Bauer and K. Pawelzik, "Quantifying the neighborhood preservation of self-organizing feature maps," IEEE Trans. Neural Netw., vol. 3, no. 4, pp. 570-579, Jul. 1992.
3. H.-U. Bauer, M. Herrmann, and T. Villmann, "Neural maps and topographic vector quantization," Neural Netw., vol. 12, pp. 659-676, 1999.
4. T. Villmann, R. Der, M. Herrmann, and T. Martinetz, "Topology preservation in self-organizing feature maps: Exact definition and measurement," IEEE Trans. Neural Netw., vol. 8, no. 2, pp. 256-266, Mar. 1997.
5. V. T. Ruoppila, T. Sorsa, and H. N. Koivo, "Recursive least-squares approach to self-organizing maps," in Proc. IEEE Int. Conf. Neural Netw., 1993, vol. 3, pp. 1480-1485.
6. K. Haese and G. J. Goodhill, "Auto- SOM: Recursive parameter estimation for guidance of self-organizing feature maps," Neural Comput., vol. 13, pp. 595-619, 2001.
7. G. Goodhill and T. Sejnowski, "Quantifying neighbourhood preservation in topographic mappings," in Proc. 3rd Joint Symp. Neural Comput. 1996, pp. 61-82.
8. S. Kaski and K. Lagus, "Comparing self-organizing maps," in Lecture Notes in Computer Science, ser. 1112, C. von der Malsburg and B. Sendholf, Eds. Berlin, Germany: Springer-Verlag, 1996, pp. 809-814.
9. I. T. Jolliffe, Principal Component Analysis, 2nd ed. New York: Springer-Verlag, 2002.
10. D. Newman, S. Hettich, C. Blake, and C. Merz, Univ. California Irvine (UCI) Repository Mach. Learn. Databases 1998 [Online]. Available: http://www.ics.uci.edu/~mlearn/mlrepository.html]
11. M. M. Van Hulle, "Kernel-based equiprobabilistic topographic map formation," Neural Comput., vol. 10, no. 7, pp. 1847-1871, 1998.
12. J. Muruzábal and S. Vegas-Azcárate, M. Cottrell, Ed., "On equiprobabilistic maps and plausible density estimation," in 5th Workshop On Self-Organizing Maps, 2005, pp. 155-162.
13. M. M. Van Hulle, Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization. New York: Wiley, 2000.
14. T. Kostiainen and J. Lampinen, "On the generative probability density model in the self-organizing map," Neurocomput., vol. 48, pp. 217-228, Oct. 2002.
15. J. Lampinen and T. Kostiainen, "Overtraining and model selection with the self-organizing map," in Proc. IJCNN, 1999, CR-ROM.
16. C. M. Bishop, M. Svensén, and C. K. I. Williams, "GTM: The generative topographic mapping," Neural Comput., vol. 10, pp. 215-235, 1997.