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Sorting with Self-Organizing Maps

Marco Budinich
1995 Neural Computation  
Self organizing maps: Order- ing, convergence properties and energy functions. Biol. Cybernet. 67, 47-55. Knuth, D. E., 1981. The Art of Computer Programming—Volume III Sorting and Searching, 2nd ed.  ...  Communicated by Graeme Mitchison Sorting with Self-Organizing Maps Marco Budinich Dipartimento di Fisica and INFN, Via Valerio 2, 34127 Trieste, Italy A self-organizing feature map (Von der Malsburg 1973  ... 
doi:10.1162/neco.1995.7.6.1188 pmid:7584897 fatcat:cjsmlms3gfa7nf3sfiaws7mqfe

Winner-Relaxing Self-Organizing Maps

Jens Christian Claussen
2005 Neural Computation  
A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991.  ...  The Winner Relaxing Algorithm requires minimal extra computations per learning step and is conveniently easy to implement.  ...  Schuster for raising attention to the topic and for stimulating discussions.  ... 
doi:10.1162/0899766053491922 fatcat:jloox66fk5ff7mxn3lvu4tn4hu

Self-Organizing Map-based Applications in Remote Sensing [chapter]

Anthony Filippi, Iliyana Dobreva, Andrew G., John R.
2010 Self-Organizing Maps  
Supervised LVQ applications in remote sensing The Kohonen self-organizing feature map, also referred to as the Kohonen clustering network (KCN) (Lin and Lee, 1996) , exhibits some advantageous properties  ...  Although an artificial convergence criterion was used during self-organization, favorable results were still obtained.  ...  Self-Organizing Map-based Applications in Remote Sensing, Self-Organizing Maps, George K Matsopoulos (Ed.), ISBN: 978-953-307-074-2, InTech, Available from: http://www.intechopen.com/books/self-organizing-maps  ... 
doi:10.5772/9163 fatcat:g7w2olupkbeg5bv4clw5numv6q

Randomized Self Organizing Map [article]

Nicolas P. Rougier, Georgios Is. Detorakis
2020 arXiv   pre-print
We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.  ...  We propose a variation of the self organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies  ...  A er learning has converged, the codebook is self-organized such that the prototypes associated with two nearby nodes are similar. is is a direct consequence of the underlying topology of the map as well  ... 
arXiv:2011.09534v1 fatcat:7wfvrub6krbgzfawmhyystfkhm

Randomized Self-Organizing Map

Nicolas P. Rougier, Georgios Is. Detorakis
2021 Neural Computation  
We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.  ...  We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies  ...  After learning has converged, the codebook is self-organized such that the prototypes associated with two nearby nodes are similar.  ... 
doi:10.1162/neco_a_01406 pmid:34310672 fatcat:eqr7j5v7ebf3bncewl5bzg6wme

Transition times in self-organizing maps

Tom M. Heskes
1996 Biological cybernetics  
Mappings in one and two dimensions are used as examples.  ...  For this study we consider a self-organizing learning rule which is equivalent to the Kohonen learning rule, except for the determination of the 'winning' unit.  ...  I would like to thank Andrzej Komoda, Eddy Slijpen and Stan Gielen for critically reading a previous version of this manuscript. I am also grateful to Prof.  ... 
doi:10.1007/s004220050273 fatcat:uixdjeynafaztnnbimw3kfhplu

Self-organizing maps with information theoretic learning

Rakesh Chalasani, Jose C. Principe
2015 Neurocomputing  
The self organizing map (SOM) is one of the popular clustering and data visualization algorithms and has evolved as a useful tool in pattern recognition, data mining since it was first introduced by Kohonen  ...  We show that the * Corresponding author proposed model can achieve a mapping with optimal magnification and can automatically adapt the parameters of the kernel function.  ...  We thank Evan Kriminger for his valuable comments and suggestions.  ... 
doi:10.1016/j.neucom.2013.12.059 fatcat:xdsycfm7drcwlo3fqyqyalzlcm

Self-Organizing Maps: A Powerful Tool for the Atmospheric Sciences [chapter]

Natasa Skific, Jennifer Francis
2012 Applications of Self-Organizing Maps  
Dalian, China, 41-45, ISSN 0272-1708 Self-Organizing Maps: A Powerful Tool for the Atmospheric Sciences http://dx.doi.org/10.5772/54299 (a) Self-Organizing Maps: A Powerful Tool for the Atmospheric  ...  Introduction Self-organizing maps (SOMs) are a powerful tool used to extract obscure diagnostic information from large datasets.  ... 
doi:10.5772/54299 fatcat:uas77apqrbhxpecsphhwugxcwq

Model-Based Clustering by Probabilistic Self-Organizing Maps

Shih-Sian Cheng, Hsin-Chia Fu, Hsin-Min Wang
2009 IEEE Transactions on Neural Networks  
map (PbSOM), self-organizing map (SOM).  ...  In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters  ...  enforce the self-organizing of the mixture components.  ... 
doi:10.1109/tnn.2009.2013708 pmid:19342347 fatcat:662woerkpbf4jfnq6pv4wvv2we

Essentials of the self-organizing map

Teuvo Kohonen
2013 Neural Networks  
The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics.  ...  This organization, a kind of similarity diagram of the models, makes it possible to obtain an insight into the topographic relationships of data, especially of high-dimensional data items.  ...  Merja Oja has kindly provided the picture and associated material about the more recent HERV studies.  ... 
doi:10.1016/j.neunet.2012.09.018 pmid:23067803 fatcat:6dpy4shlfvaqhc4vm2o73ytjx4

Using Wavelets for Feature Extraction and Self Organizing Maps for Fault Diagnosis of Nonlinear Dynamic Systems [chapter]

Hector Benitez-Perez, Jorge L., Alma Benitez-
2012 Applications of Self-Organizing Maps  
Acknowledgements The authors would like to thank the financial support of DISCA-IIMAS-UNAM, ICYTDF PICCO10-53, and UNAM-PAPIIT (IN103310) Mexico, in connection with this work.  ...  Wavelets for Feature Extraction and Self Organizing Maps for Fault Diagnosis of Nonlinear Dynamic Systems http://dx.doi.org/10.5772/50235 Figure 1.  ...  Self Organizing Maps (SOM) The purpose of Kohonen's SOM is to capture the topology and probability distribution of some input data (Figure 1 ) [13][14] .  ... 
doi:10.5772/50235 fatcat:atk3yaowonebhkp7fqkbtyndt4

Fault tolerance of self organizing maps

Cesar Torres-Huitzil, Oleksandr Popovych, Bernard Girau
2017 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)  
In this paper, the fault tolerance properties of Self Organizing Maps (SOMs) are investigated.  ...  Beyond energy, the growing number of defects in physical substrates is becoming another major constraint that affects the design of computing devices and systems.  ...  This work is also partially supported by Erasmus+ 2016-2017 and by the French embassy of Ukraine.  ... 
doi:10.1109/wsom.2017.8020001 dblp:conf/wsom/Torres-HuitzilP17 fatcat:isovuz7vwnhy3bhwhcqy6ba4ju

Theoretical and Applied Aspects of the Self-Organizing Maps [chapter]

Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Nathalie Villa-Vialaneix
2016 Advances in Intelligent Systems and Computing  
The Self-Organizing Map (SOM) is widely used, easy to implement, has nice properties for data mining by providing both clustering and visual representation.  ...  However, since its conception, the mathematical study of the SOM remains difficult and has be done only in very special cases.  ...  Kohonen in his seminal 1982 articles (Kohonen [1982a,b] ), the self-organizing map (SOM) algorithm has encountered a very large success.  ... 
doi:10.1007/978-3-319-28518-4_1 fatcat:xegpuygvsja57am7nddag2wkx4

On the Ordering Conditions for Self-Organizing Maps

Marco Budinich, John G. Taylor
1995 Neural Computation  
Erwin, E., Obermayer, K., and Schulten, K. 1992. Self organizing maps: Order- ing, convergence properties and energy functions. Biol. Cybern. 67, 47-55. Kohonen, T. 1989.  ...  Communicated by Peter Dayan On the Ordering Conditions for Self-Organizing Maps Marco Budinich' John G.  ... 
doi:10.1162/neco.1995.7.2.284 fatcat:u5bxit2qrjgvhcm526cdt2gk3i

Wireless localization using self-organizing maps

Gianni Giorgetti, Sandeep K. S. Gupta, Gianfranco Manes
2007 Proceedings of the 6th international conference on Information processing in sensor networks - IPSN '07  
The approach, suitable for deployments with strict cost constraints, is based on the neural network paradigm of Self-Organizing Maps (SOM).  ...  We analytically demonstrate that the proposed scheme has low computation and communication overheads; hence, making it suitable for resource-constrained networks.  ...  We would like to thank Guofeng Deng, Kari Torkkola, Jie Gao and anonymous reviewers for their helpful comments.  ... 
doi:10.1145/1236360.1236399 dblp:conf/ipsn/GiorgettiGM07 fatcat:bd5hjl554javvdpzkmk3lexxqq
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