Batch neural gas

WSOM 2005 - 5th Workshop on Self-Organizing Maps

Volumn , Issue , 2005, Pages 275-282

  Cottrell, Marie ^a   Hammer, Barbara ^b   Hasenfuß, Alexander ^b   Villmann, Thomas ^c  

^a University Paris I Sorbonne Panthéon   (France)

^b CLAUSTHAL UNIVERSITY OF TECHNOLOGY   (Germany)

^c UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

Batch algorithm; Cost function; Neural gas; Proximity data

Indexed keywords

BATCH ALGORITHMS; BATCH NEURAL GAS; FASTER CONVERGENCE; NEURAL GAS; PROXIMITY DATA;

CLUSTERING ALGORITHMS; COST FUNCTIONS;

CONFORMAL MAPPING;

EID: 84890264789     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. M. Belkin and P. Niyogi (2002), Laplacian eigenmaps and spectral techniques for embedding and clustering, NIPS.
2. C.L. Blake and C.J. Merz (1998), UCI Repository of machine learning databases, Irvine, CA, University of California, Department of Information and Computer Science.
3. I. Borg and P. Groenen (1997), Modern multidimensional scaling: theory and applications, Springer.
4. L. Bottou and Y. Bengio (1995), Convergence properties of the k-means algorithm, NIPS.
5. Y. Cheng (1997), Convergence and ordering of Kohonen's batch map, Neur. Comp. 9: 1667-1676.
6. M. Cottrell, J.C. Fort, and G. Pagès (1999), Theoretical aspects of the SOM algorithm, Neurocomputing 21: 119-138.
7. M. Cottrell, S. Ibbou, and P. Letrémy (2004), SOM-based algorithms for qualitative variables, Neural Networks 17: 1149-1168.
8. R.O. Duda, P.E. Hart, and D.G. Storck (2000), Pattern Classification, Wiley.
9. J.-C. Fort, P. Letrémy, and M. Cottrell (2002), Advantages and drawbacks of the Batch Kohonen algorithm, in ESANN'2002, M. Verleysen (ed.), 223-230, D Facto.
10. T. Graepel and K. Obermayer (1999), A self-organizing map for proximity data, Neur. Comp. 11: 139-155.
11. B. Hammer, M. Strickert, and T. Villmann (2005), Supervised neural gas with general similarity measure, Neural Processing Letters 21: 21-44.
12. T. Heskes (2001), Self-organizing maps, vector quantization, and mixture modeling, IEEE Transactions on Neural Networks, 12: 1299-1305.
13. T. Kohonen (1995), Self-Organizing Maps, Springer.
14. T. Kohonen and P. Somervuo (2002), How to make large self-organizing maps for nonvectorial data, Neural Networks 15: 945-952.
15. T. Martinetz, S.G. Berkovich, and K.J. Schulten (1993), 'Neural-gas' network for vector quantization and its application to time-series prediction, IEEE Transactions on Neural Networks 4: 558-569.
16. T. Martinetz and K.J. Schulten (1994), Topology representing networks, Neural Networks 7: 507-522.
17. M.N. Murty, A.K. Jain, and P.J. Flynn (1999), Data clustering: a review, ACM Comp. Surv. 31: 264-323.
18. Neural Networks Research Centre, Helsinki University of Technology, SOM Toolbox, http://www.cis.hut.fi/projects/somtoolbox/
19. B.D. Ripley (1996), Pattern Recognition and Neural Networks, Cambridge.
20. S. Seo and K. Obermayer (2004), Self-organizing maps and clustering methods for matrix data, Neural Networks 17: 1211-1230.
21. H. Mevissen and M. Vingron (1996), Quantifying the local reliability of a sequence alignment, Protein Engineering 9: 127-132.
22. T. Villmann, R. Der, M. Herrmann, and T. Martinetz (1994), Topology preservation in self-organizing feature maps: exact definition and measurement, IEEE Transactions on Neural Networks 2: 256-266.