A self-organizing map, or self-organizing feature map, is an unsupervised machine learning technique that produces a low-dimensional, typically two-dimensional, representation of a higher-dimensional data set while preserving the data's topological structure, making high-dimensional data easier to visualize and analyze. It is a type of artificial neural network, but unlike other artificial neural networks it is trained by competitive learning rather than error-correction learning such as backpropagation with gradient descent, and it was introduced by the Finnish professor Teuvo Kohonen in the 1980s, so it is sometimes called a Kohonen map or Kohonen network.
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Self-organizing map (Wikipedia)
Credited To: Teuvo Kohonen, Lead section
The SOM was introduced by the Finnish professor Teuvo Kohonen in the 1980s
Invented By: Teuvo Kohonen, Lead section
The SOM was introduced by the Finnish professor Teuvo Kohonen in the 1980s
In Field: Machine Learning, Lead
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
In Group: Neural Network Training and Representation Algorithms, Lead
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
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