Developments and applications of the self-organizing map and related algorithms

In this paper the basic principles and developments of an unsupervised learning algorithm, the self-organizing map (SOM) and a supervised learning algorithm, the learning vector quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visualization and pattern recognition tasks are mentioned. At the end of the paper new results are reported about increased error tolerance in the transmission of vector quantized images, provided by the topological ordering of codewords by the SOM algorithm.

[1]  Olli Simula,et al.  Process State Monitoring Using Self-Organizing Maps , 1992 .

[2]  Jorma Laaksonen,et al.  LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[3]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[4]  D. S. Bradburn.  Reducing transmission error effects using a self-organizing network , 1989, International 1989 Joint Conference on Neural Networks.

[5]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[6]  Stephen P. Luttrell,et al.  Derivation of a class of training algorithms , 1990, IEEE Trans. Neural Networks.

[7]  Teuvo Kohonen,et al.  The 'neural' phonetic typewriter , 1988, Computer.

[8]  Anil K. Jain,et al.  Image data compression: A review , 1981, Proceedings of the IEEE.

[9]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[10]  Mikko Kurimo,et al.  Status Report Of The Finnish Phonetic Typewriter Project , 1991 .

[11]  Juha Tuominen,et al.  PROCESS ERROR DETECTION USING SELF-ORGANIZING FEATURE MAPS , 1991 .

[12]  Olli Simula,et al.  Operational Cloud Classifier Based on the Topological Feature Map , 1993 .

[13]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[14]  G. L. Sicuranza,et al.  Application of ordered codebooks to image coding , 1993, Neural Networks for Signal Processing III - Proceedings of the 1993 IEEE-SP Workshop.

[15]  Nasser M. Nasrabadi,et al.  Image coding using vector quantization: a review , 1988, IEEE Trans. Commun..

[16]  Nasser M. Nasrabadi,et al.  Vector quantization of images based upon the Kohonen self-organizing feature maps , 1988, ICNN.

[17]  Olli Simula,et al.  Self-Organizing Feature Maps in Texture Classification and Segmentation , 1992 .

[18]  Teuvo Kohonen,et al.  Improved versions of learning vector quantization , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[19]  Samuel Kaski,et al.  Monitoring EEG Signal with the Self-Organizing Map , 1993 .

[20]  L. Rabiner,et al.  The acoustics, speech, and signal processing society - A historical perspective , 1984, IEEE ASSP Magazine.

[21]  Les E. Atlas,et al.  A comparison of the LBG algorithm and Kohonen neural network paradigm for image vector quantization , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[22]  S. Carrato.  Image vector quantization using ordered codebooks: Properties and applications , 1994, Signal Process..

[23]  K. Schulten,et al.  Kohonen's self-organizing maps: exploring their computational capabilities , 1988, IEEE 1988 International Conference on Neural Networks.

[24]  E. A. Riskin,et al.  Ordered neural maps and their applications to data compression , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

[25]  L Leinonen,et al.  Dysphonia detected by pattern recognition of spectral composition. , 1992, Journal of speech and hearing research.

[26]  Gilles Burel,et al.  Image compression using topological maps and MLP , 1993, IEEE International Conference on Neural Networks.

[27]  T. Kohonen,et al.  Appendix 2.4 Stopping Rule 2.3 Fine Tuning Using the Basic Lvq1 or Lvq2.1 Lvq Pak: a Program Package for the Correct Application of Learning Vector Quantization Algorithms , 1992 .

[28]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[29]  L. Tarassenko,et al.  Analysis of the sleep EEG using a multilayer network with spatial organisation , 1992 .