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Essentials of the self-organizing map

Kohonen, Teuvo

Neural networks, 2013-01, Vol.37, p.52-65 [Periódico revisado por pares]

Kidlington: Elsevier Ltd

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Título:
Essentials of the self-organizing map
Autor: Kohonen, Teuvo
Assuntos: Algorithms ; Animals ; Applied sciences ; Artificial intelligence ; Biological and medical sciences ; Brain - physiology ; Brain map ; Brain Mapping ; Computer science; control theory; systems ; Computer Simulation ; Connectionism. Neural networks ; Data analysis ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Fundamental and applied biological sciences. Psychology ; General aspects ; Humans ; Information systems. Data bases ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Memory organisation. Data processing ; Models, Neurological ; Self-organizing map ; Similarity ; Software ; SOM ; Vector quantization
É parte de: Neural networks, 2013-01, Vol.37, p.52-65
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
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Descrição: 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. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The SOM is related to the classical vector quantization (VQ), which is used extensively in digital signal processing and transmission. Like in VQ, the SOM represents a distribution of input data items using a finite set of models. In the SOM, however, these models are automatically associated with the nodes of a regular (usually two-dimensional) grid in an orderly fashion such that more similar models become automatically associated with nodes that are adjacent in the grid, whereas less similar models are situated farther away from each other in the grid. 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. If the data items belong to certain predetermined classes, the models (and the nodes) can be calibrated according to these classes. An unknown input item is then classified according to that node, the model of which is most similar with it in some metric used in the construction of the SOM. A new finding introduced in this paper is that an input item can even more accurately be represented by a linear mixture of a few best-matching models. This becomes possible by a least-squares fitting procedure where the coefficients in the linear mixture of models are constrained to nonnegative values.
Editor: Kidlington: Elsevier Ltd
Idioma: Inglês

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Essentials of the self-organizing map

Kohonen, Teuvo

Kidlington: Elsevier Ltd

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