Self-organizing maps

Handbook of Natural Computing

Volumn 1-4, Issue , 2012, Pages 585-622

  Van Hulle, Marc M ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; CONFORMAL MAPPING; DATA HANDLING; MAPS; TREES (MATHEMATICS);

CATEGORICAL DATA; HIGH DIMENSIONAL DATA; NONLINEAR APPROXIMATION; SOM ALGORITHMS; TOPOGRAPHIC MAP; TREE-STRUCTURED DATA;

SELF ORGANIZING MAPS;

EID: 84896944956     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-540-92910-9_19     Document Type: Chapter

References (117)

1. Abrantes AJ, Marques JS (1995) Unified approach to snakes, elastic nets, and Kohonen maps. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'95). IEEE Computer Society, Los Alamitos, CA, vol 5, pp 3427-3430
2. Ahalt SC, Krishnamurthy AK, Chen P, Melton DE (1990) Competitive learning algorithms for vector quantization. Neural Netw 3:277-290
3. Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self organising maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw (Special issue on knowledge discovery and data mining) 11(3):601-614
4. Axelson D, Bakken IJ, Gribbestad IS, Ehrnholm B, Nilsen G, Aasly J (2002) Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of Parkinson disease patients. J Magn Reson Imaging 16(1):13-20
5. Ball KD, Erman B, Dill KA (2002) The elastic net algorithm and protein structure prediction. J Comput Chem 23(1):77-83
6. Barreto G, Araú jo A (2001) Time in self-organizing maps: an overview of models. Int J Comput Res 10(2):139-179
7. Bauer H-U, Villmann T (1997) Growing a hypercubical output space in a self-organizing feature map. IEEE Trans Neural Netw 8(2):218-226
8. Bauer H-U, Der R, Herrmann M (1996) Controlling the magnification factor of self-organizing feature maps. Neural Comput 8:757-771
9. Benaim M, Tomasini L (1991) Competitive and selforganizing algorithms based on the minimization of an information criterion. In: Proceedings of 1991 international conference in artificial neural networks (ICANN'91). Espoo, Finland. Elsevier Science Publishers, North-Holland, pp 391-396
10. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
11. Bishop CM, Svensén M, Williams CKI (1996) GTM: a principled alternative to the self-organizing map. In: Proceedings 1996 International Conference on Artificial Neural Networks (ICANN'96). Bochum, Germany, 16-19 July 1996. Lecture notes in computer science, vol 1112. Springer, pp 165-170
12. Bishop CM, Hinton GE, and Strachan IGD (1997) In:Proceedings IEE fifth international conference on artificial neural networks. Cambridge UK, 7-9 July 1997, pp 111-116
13. Bishop CM, Svensén M, Williams CKI (1998) GTM: the generative topographic mapping. Neural Comput 10:215-234
14. Blackmore J, Miikkulainen R (1993) Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In: Proceedings of IEEE international conference on neural networks. San Francisco, CA. IEEE Press, Piscataway, NJ, vol 1, pp 450-455
15. Bruske J, Sommer G (1995) Dynamic cell structure learns perfectly topology preserving map. Neural Comput 7(4):845-865
16. Carreira-Perpiñán MÁ, Renals S (1998) Dimensionality reduction of electropalatographic data using latent variable models. Speech Commun 26(4):259-282
17. Centre NNR (2003) Bibliography on the Self-Organizing Map (SOM) and LearningVectorQuantization (LVQ), Helsinki University of Technology. http://liinwww.ira.uka.de/bibliography/Neural/SOM. LVQ.html
18. Chappell G, Taylor J (1993) The temporal Kohonen map. Neural Netw 6:441-445
19. Chinrungrueng C, Séquin CH (1995) Optimal adaptive k-means algorithm with dynamic adjustment of learning rate. IEEE Trans Neural Netw 6:157-169
20. Cottrell M, Fort JC (1987) Etude d'un processus d'autoorganization. Ann Inst Henri Poincaré 23:1-20
21. D'Alimonte D, Lowe D, Nabney IT, Sivaraksa M (2005) Visualising uncertain data. In: Proceedings European conference on emergent aspects in clinical data analysis (EACDA2005). Pisa, Italy, 28-30 September 2005. http://ciml.di.unipi.it/EACDA2005/papers.html
22. Deleus FF, Van Hulle MM (2001) Science and technology interactions discovered with a new topographic map-based visualization tool. In: Proceedings of 7th ACM SIGKDD international conference on knowledge discovery in data mining. San Francisco, 26-29 August 2001. ACMPress, New York, pp 42-50
23. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood for incomplete data via the EM algorithm. J R Stat Soc B 39:1-38
24. Der R, Herrmann M (1993) Phase transitions in selforganizing feature maps. In: Proceedings of 1993 international conference on artificial neuron networks (ICANN'93). Amsterdam, The Netherlands, 13-16 September 1993, Springer, New York, pp 597-600
25. DeSieno D (1988) Adding a conscience to competitive learning. In: Proceedings of IEEE international conference on neural networks. San Diego, CA, IEEE Press, New York, vol I, pp 117-124
26. Durbin R, Willshaw D (1987) An analogue approach to the travelling salesman problem using an elastic net method. Nature 326:689-691
27. Durbin R, Szeliski R, Yuille AL (1989) An analysis of the elastic net approach to the traveling salesman problem. Neural Comput 1:348-358
28. Erwin E, Obermayer K, Schulten K (1992) Selforganizing maps: ordering, convergence properties and energy functions. Biol Cybern 67:47-55
29. Euliano NR, Principe JC (1999). A spatiotemporal memory based on SOMs with activity diffusion. In:Oja E, Kaski S (eds) Kohonen maps. Elsevier, Amsterdam, The Netherlands, pp 253-266
30. Fritzke B (1994) Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Netw 7(9):1441-1460
31. Fritzke B (1995a) A growing neural gas network learns topologies. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information proceedings systems 7 (NIPS 1994). MIT Press, Cambridge, MA, pp 625-632
32. Fritzke B (1995b) Growing grid-a self-organizing network with constant neighborhood range and adaptation strength. Neural Process Lett 2(5):9-13
33. Fritzke B (1996) Growing self-organizing networks-why? In: European symposium on artificial neural networks (ESANN96). Bruges, Belgium, 1996. D Facto Publications, Brussels, Belgium, pp 61-72
34. Gautama T, Van Hulle MM (2006) Batch map extensions of the kernel-based maximum entropy learning rule. IEEE Trans Neural Netw 16(2):529-532
35. Gersho A, Gray RM (1991) Vector quantization and signal compression. Kluwer, Boston, MA/Dordrecht
36. Geszti T (1990) Physical models of neural networks. World Scientific Press, Singapore
37. Gilson SJ, Middleton I, Damper RI (1997) A localised elastic net technique for lung boundary extraction from magnetic resonance images. In: Proceedings of fifth international conference on artificial neural networks. Cambridge, UK, 7-9 July 1997. Mascarenhas Publishing, Stevenage, UK, pp 199-204
38. Gorbunov S, Kisel I (2006) Elastic net for stand-alone RICH ring finding. Nucl Instrum Methods Phys Res A 559:139-142
39. Graepel T, Burger M, Obermayer K (1997) Phase transitions in stochastic self-organizing maps. Phys Rev E 56(4):3876-3890
40. Graepel T, Burger M, Obermayer K (1998) Self-organizing maps: generalizations and new optimization techniques. Neurocomputing 21:173-190
41. Grossberg S (1976) Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biol Cybern 23:121-134
42. Günter S, Bunke H (2002) Self-organizing map for clustering in the graph domain, Pattern Recog Lett 23:415-417
43. Hagenbuchner M, Sperduti A, Tsoi AC (2003) A selforganizing map for adaptive processing of structured data. IEEE Trans Neural Netw 14(3):491-505
44. Hammer B, Micheli A, Strickert M, Sperduti A (2004) A general framework for unsupervised processing of structured data. Neurocomputing 57:3-35
45. Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005) Self organizing maps for time series. In: Proceedings of WSOM 2005. Paris, France, 5-8 September 2005, pp 115-122
46. Heskes T (2001) Self-organizing maps, vector quantization, and mixture modeling. IEEE Trans Neural Netw 12(6):1299-1305
47. Heskes TM, Kappen B (1993) Error potentials for selforganization. In: Proceedings of IEEE international conference on neural networks. San Francisco, CA. IEEE Press, Piscataway, NJ, pp 1219-1223
48. Jin B, Zhang Y-Q, Wang B (2005) Evolutionary granular kernel trees and applications in drug activity comparisons, In: Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'05). San Diego, CA, 14-15 November 2005, IEEE Press, Piscataway, NY, pp 1-6
49. Kabán A (2005) A scalable generative topographic mapping for sparse data sequences. In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05). Las Vegas, NV, 4-6 April 2005. IEEE Computer Society, vol 1, pp 51-56
50. Kass M, Witkin A, Terzopoulos D (1987) Active contour models. Int J Comput Vis 1(4):321-331
51. Kangas J (1990) Time-delayed self-organizing maps. In: Proceedings IEEE/INNS international Joint Conference on neural networks 1990. San Diego, CA, IEEE, New York, vol 2, pp 331-336
52. Kaski S, Honkela T, Lagus K, Kohonen T (1998) WEBSOM-self-organizing maps of document collections. Neurocomputing 21:101-117
53. Kim YK, Ra JB (1995) Adaptive learning method in selforganizing map for edge preserving vector quantization. IEEE Trans Neural Netw 6:278-280
54. Kitamoto A (2002) Evolution map:modeling state transition of typhoon image sequences by spatio-temporal clustering. Lect Notes Comput Sci 2534/2002:283-290
55. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59-69
56. Kohonen T (1984) Self-organization and associative memory. Springer, Heidelberg
57. Kohonen T (1991) Self-organizing maps: optimization approaches. In: Kohonen T, Mäkisara K, Simula O, Kangas J (eds) Artificial neural networks. North-Holland, Amsterdam, pp 981-990
58. Kohonen T (1995) Self-organizing maps, 2nd edn. Springer, Heidelberg
59. Kohonen T (1997) Self-organizing maps. Springer
60. Kohonen T, Somervuo P (1998) Self-organizing maps on symbol strings. Neurocomputing 21:19-30
61. Kohonen T, Kaski S, Salojärvi J, Honkela J, Paatero V, Saarela A (1999) Self organization of a massive document collection. IEEE Trans Neural Netw 11(3):574-585
62. Koskela T, Varsta M, Heikkonen J, Kaski K (1998) Recurrent SOM with local linear models in time series prediction. In: Verleysen M (ed) Proceedings of 6th European symposium on artificial neural networks (ESANN 1998). Bruges, Belgium, April 22-24, 1998. D-Facto, Brussels, Belgium, pp 167-172
63. Kostiainen T, Lampinen J (2002) Generative probability density model in the self-organizing map. In: Seiffert U, Jain L (eds) Self-organizing neural networks:Recent advances and applications. Physica-Verlag, Heidelberg, pp 75-94
64. Laaksonen J, Koskela M, Oja E (2002) PicSOM-selforganizing image retrieval with MPEG-7 content descriptors. IEEE Trans Neural Netw 13(4):841-853
65. Lin JK, Grier DG, Cowan JD (1997) Faithful representation of separable distributions. Neural Comput 9:1305-1320
66. Linsker R (1988) Self-organization in a perceptual network. Computer 21:105-117
67. Linsker R (1989) How to generate ordered maps by maximizing the mutual information between input and output signals. Neural Comput 1:402-411
68. Luttrell SP (1989) Self-organization: a derivation from first principles of a class of learning algorithms. In: Proceedings IEEE international joint conference on neural networks (IJCNN89). Washington, DC, Part I, IEEE Press, Piscataway, NJ, pp 495-498
69. Luttrell SP (1990) Derivation of a class of training algorithms. IEEE Trans Neural Netw 1:229-232
70. Luttrell SP (1991) Code vector density in topographic mappings: scalar case. IEEE Trans Neural Netw 2:427-436
71. Martinetz TM (1993) Competitive Hebbian learning rule forms perfectly topology preserving maps. In: Proceedings of international conference on artificial neural networks (ICANN93). Amsterdam, The Netherlands, 13-16 September 1993. Springer, London, pp 427-434
72. Martinetz T, Schulten K (1991) A ''neural-gas'' network learns topologies. In: Kohonen T, Mäkisara K, Simula O, Kangas J (eds) Proceedings of International Conference on Artificial Neural Networks (ICANN-91). Espoo, Finland, 24-28 June 1991, vol I, North-Holland, Amsterdam, The Netherlands, pp 397-402
73. Martinetz T, Berkovich S, Schulten K (1993) ''Neuralgas'' network for vector quantization and its application to time-series prediction. IEEE Trans Neural Netw 4(4):558-569
74. Merkl D, He S, Dittenbach M, Rauber A (2003) Adaptive hierarchical incremental grid growing: an architecture for high-dimensional data visualization. In: Proceedings of 4th workshop on self-organizing maps (WSOM03). Kitakyushu, Japan, 11-14 September 2003
75. Mulier F, Cherkassky V (1995) Self-organization as an iterative kernel smoothing process. Neural Comput 7:1165-1177
76. Rauber A, Merkl D, Dittenbach M (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans Neural Netw 13(6):1331-1341
77. Redner RA, Walker HF (1984) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26(2):195-239
78. Risi S, Mörchen F, Ultsch A, Lewark P (2007) Visual mining in music collections with emergent SOM. In: Proceedings of workshop on self-organizing maps (WSOM '07). Bielefeld, Germany, September 3-6, 2007, ISBN: 978-3-00-022473-7, CD ROM, available online at http://biecoll.ub.uni-bielefeld.de
79. Ritter H (1991) Asymptotic level density for a class of vector quantization processes. IEEE Trans Neural Netw 2(1):173-175
80. Ritter H (1998) Self-organizing maps in non-Euclidean spaces. In: Oja E, Kaski S (eds) Kohonen maps. Elsevier, Amsterdam, pp 97-108
81. Ritter H, Kohonen T (1989) Self-organizing semantic maps. Biol Cybern 61:241-254
82. Ritter H, Schulten K (1986) On the stationary state of Kohonen's self-organizing sensory mapping. Biol Cybern 54:99-106
83. Ritter H, Schulten K (1988) Kohonen's self-organizing maps: exploring their computational capabilities, In:Proceedings of IEEE international conference on neural networks (ICNN). San Diego, CA. IEEE, New York, vol I, pp 109-116
84. Ritter H, Martinetz T, Schulten K (1992) Neural computation and self-organizing maps: an introduction. Addison-Wesley, Reading, MA
85. Rose K, Gurewitz E, Fox GC (1993) Constrained clustering as an optimization method. IEEE Trans Pattern Anal Mach Intell 15(8):785-794
86. Schulz R, Reggia JA (2004) Temporally asymmetric learning supports sequence processing in multiwinner self-organizing maps. Neural Comput 16(3):535-561
87. Seo S, Obermayer K (2004) Self-organizing maps and clustering methods for matrix data. Neural Netw 17(8-9):1211-1229
88. Shawe-Taylor J, Cristianini N (2004) Kernel methods in computational biology. MIT Press, Cambridge, MA
89. Simon G, Lendasse A, Cottrell M, Fort J-C, Verleysen M (2003) Double SOM for long-term time series prediction. In: Proceedings of the workshop on selforganizing maps (WSOM 2003). Hibikino, Japan, September 11-14, 2003, pp 35-40
90. Somervuo PJ (2004) Online algorithm for the selforganizing map of symbol strings. Neural Netw 17(8-9):1231-1240
91. Steil JJ, Sperduti A (2007) Indices to evaluate selforganizing maps for structures. In: Proceedings of the workshop on self-organizing maps (WSOM07) Bielefeld, Germany, 3-6 September 2007. CD ROM, 2007, available online at http://biecoll.ub.uni-bielefeld.de
92. Strickert M, Hammer B (2003a) Unsupervised recursive sequence processing, In: Verleysen M (ed) European Symposium on Artificial Neural Networks (ESANN 2003). Bruges, Belgium, 23-25 April 2003. D-Side Publications, Evere, Belgium, pp 27-32
93. Strickert M, Hammer B (2003b) Neural gas for sequences. In: Proceedings of the workshop on self-organizing maps (WSOM'03). Hibikino, Japan, September 2003, pp 53-57
94. Strickert M, Hammer B (2005) Merge SOM for temporal data. Neurocomputing 64:39-72
95. Tiňo P, Nabney I (2002) Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way. IEEE Trans Pattern Anal Mach Intell 24(5):639-656
96. Tiňo P, Kabán A, Sun Y (2004) A generative probabilistic approach to visualizing sets of symbolic sequences. In: Kohavi R, Gehrke J, DuMouchel W, Ghosh J (eds) Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2004), Seattle, WA, 22-25 August 2004. ACM Press, New York, pp 701-706
97. Tolat V (1990) An analysis of Kohonen's self-organizing maps using a system of energy functions. Biol Cybern 64:155-164
98. Ultsch A, Siemon HP (1990) Kohonen's self organizing feature maps for exploratory data analysis. In: Proceedings international neural networks. Kluwer, Paris, pp 305-308
99. Ultsch A, Mörchen F (2005) ESOM-Maps: Tools for clustering, visualization, and classification with emergent SOM. Technical Report No. 46, Department of Mathematics and Computer Science, University of Marburg, Germany
100. Ueda N, Nakano R (1993) A new learning approach based on equidistortion principle for optimal vector quantizer design. In: Proceedings of IEEE NNSP93, Linthicum Heights, MD. IEEE, Piscataway, NJ, pp 362-371
101. Van den Bout DE, Miller TK III (1989) TInMANN:the integer Markovian artificial neural network. In: Proceedings of international joint conference on neural networks (IJCNN89). Washington, DC, 18-22 June 1989, Erlbaum, Englewood Chifts, NJ, pp II205-II211
102. Van Hulle MM (1997a) Topology-preserving map formation achieved with a purely local unsupervised competitive learning rule. Neural Netw 10(3):431-446
103. Van Hulle MM (1997b) Nonparametric density estimation and regression achieved with topographic maps maximizing the information-theoretic entropy of their outputs. Biol Cybern 77:49-61
104. Van Hulle MM (1998) Kernel-based equiprobabilistic topographic map formation. Neural Comput 10(7):1847-1871
105. Van Hulle MM (2000) Faithful representations and topographic maps: from distortion-to informationbased self-organization. Wiley, New York
106. Van Hulle MM (2002a) Kernel-based topographic map formation by local density modeling. Neural Comput 14(7):1561-1573
107. Van Hulle MM (2002b) Joint entropy maximization in kernel-based topographic maps. Neural Comput 14(8):1887-1906
108. Van HulleMM (2005a) Maximumlikelihood topographic map formation. Neural Comput 17(3):503-513
109. Van Hulle MM (2005b) Edgeworth-expanded topographic map formation. In: Proceedings of workshop on self-organizing maps (WSOM05). Paris, France, 5-8 September 2005, pp 719-724
110. Van Hulle MM (2009) Kernel-based topographic maps:theory and applications. In: Wah BW (ed) Encyclopedia of computer science and engineering. Wiley, Hoboken, vol 3, pp 1633-1650
111. Van Hulle MM, Gautama T (2004) Optimal smoothing of kernel-based topographic maps with application to density-based clustering of shapes. J VLSI Signal Proces Syst Signal, Image, Video Technol 37:211-222
112. Verbeek JJ, Vlassis N, Kröse BJA (2005) Self-organizing mixture models. Neurocomputing 63:99-123
113. Vesanto J (1997) Using the SOM and local models in time-series prediction. In: Proceedings of workshop on self-organizing maps (WSOM 1997). Helsinki, Finland, 4-6 June 1997. Helsinki University of Technology, Espoo, Finland, pp 209-214
114. Voegtlin T (2002) Recursive self-organizing maps. Neural Netw 15(8-9):979-992
115. Wiemer JC (2003) The time-organized map algorithm:extending the self-organizing map to spatiotemporal signals. Neural Comput 15(5):1143-1171
116. Willshaw DJ, von der Malsburg C (1976) How patterned neural connections can be set up by self-organization. Proc Roy Soc Lond B 194:431-445
117. Yin H, Allinson NM (2001) Self-organizing mixture networks for probability density estimation. IEEE Trans Neural Netw 12:405-411