 Machine learning technique useful for dimensionality reduction |
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. For example, a data set with variables measured in 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.
A 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 professor in the 1980s and therefore is sometimes called a or . The Kohonen map or network is a computationally convenient abstraction building on biological models of neural systems from the 1970s and models dating back to in the 1950s. SOMs create internal representations reminiscent of the , a distorted representation of the , based on a neurological "map" of the areas and proportions of the dedicated to processing , for different parts of the body.