Editing Self-organizing map (section)

{{Short description|Machine learning technique useful for dimensionality reduction}}
{{Machine learning|Artificial neural network}}
A '''self-organizing map''' ('''SOM''') or '''self-organizing feature map''' ('''SOFM''') is an [[unsupervised learning|unsupervised]] [[machine learning]] technique used to produce a [[dimensionality reduction|low-dimensional]] (typically two-dimensional) representation of a higher-dimensional data set while preserving the [[topology|topological structure]] of the data. For example, a data set with <math>p</math> variables measured in <math>n</math> observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a two-dimensional "map" such that observations in proximal clusters have more similar values than observations in distal clusters. This can make high-dimensional data easier to visualize and analyze.

An SOM is a type of [[artificial neural network]] but is trained using [[competitive learning]] rather than the error-correction learning (e.g., [[backpropagation]] with [[gradient descent]]) used by other artificial neural networks. The SOM was introduced by the [[Finland|Finnish]] professor [[Teuvo Kohonen]] in the 1980s and therefore is sometimes called a '''Kohonen map''' or '''Kohonen network'''.<ref name="KohonenMap">{{cite journal |title= Kohonen Network |last1= Kohonen |first1= Teuvo |last2= Honkela |first2= Timo |year= 2007 |journal= Scholarpedia |volume= 2 |issue= 1 |pages= 1568 |doi= 10.4249/scholarpedia.1568 |bibcode= 2007SchpJ...2.1568K |doi-access= free }}</ref><ref>{{cite journal |last= Kohonen |first= Teuvo |year= 1982 |title= Self-Organized Formation of Topologically Correct Feature Maps |journal= Biological Cybernetics |volume= 43 |number= 1 |pages= 59–69 |doi= 10.1007/bf00337288|s2cid= 206775459 }}</ref> The Kohonen map or network is a computationally convenient abstraction building on biological models of neural systems from the 1970s<ref>{{cite journal | last1 = Von der Malsburg | first1 = C | year = 1973 | title = Self-organization of orientation sensitive cells in the striate cortex | journal = Kybernetik | volume = 14 | issue = 2| pages = 85–100 | doi=10.1007/bf00288907| pmid = 4786750 | s2cid = 3351573 }}</ref> and [[morphogenesis]] models dating back to [[Alan Turing]] in the 1950s.<ref>{{cite journal | last1 = Turing | first1 = Alan | year = 1952 | title = The chemical basis of morphogenesis | journal = Phil. Trans. R. Soc. | volume = 237 | issue = 641 | pages = 37–72 | doi=10.1098/rstb.1952.0012 | bibcode = 1952RSPTB.237...37T | doi-access =  }}</ref>
SOMs create internal representations reminiscent of the [[cortical homunculus]]{{Citation needed|date=September 2024}}, a distorted representation of the [[human body]], based on a neurological "map" of the areas and proportions of the [[human brain]] dedicated to processing [[Sensory processing|sensory function]]s, for different parts of the body.

[[File:Synapse Self-Organizing Map.png|thumb|right|300px|A self-organizing map showing [[United States Congress|U.S. Congress]] voting patterns. The input data was a table with a row for each member of Congress, and columns for certain votes containing each member's yes/no/abstain vote. The SOM algorithm arranged these members in a two-dimensional grid placing similar members closer together. '''The first plot''' shows the grouping when the data are split into two clusters. '''The second plot''' shows average distance to neighbours: larger distances are darker. '''The third plot''' predicts [[Republican Party (United States)|Republican]] (red) or [[Democratic Party (United States)|Democratic]] (blue) party membership. '''The other plots''' each overlay the resulting map with predicted values on an input dimension: red means a predicted 'yes' vote on that bill, blue means a 'no' vote. The plot was created in [[Peltarion Synapse|Synapse]].]]<!--
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