View source for Self-Organizing Map

'''Self-Organizing Map''' (SOM) is an unsupervised learning algorithm developed by [[Teuvo Kohonen]] that maps high-dimensional input data to a low-dimensional (typically two-dimensional) lattice of neurons in a topologically preserving way. It is a form of [[Competitive learning|competitive learning]] in which neurons compete to represent input patterns, and the winning neuron and its neighbors adjust their weights to better match the input.

The SOM algorithm produces a discretized representation of the input space that preserves the topological relationships of the data: similar inputs activate nearby neurons on the map. This makes SOMs useful for visualization, clustering, and dimensionality reduction. The map is "self-organizing" because the global structure emerges from local interactions between neurons and inputs, without external supervision.

In the context of [[Adaptive resonance|adaptive resonance theory]] and other neural network architectures, the SOM represents a distinct approach to [[Category Learning|category learning]]: categories are not predefined but emerge from the geometry of the data manifold, encoded in the spatial organization of the neural lattice.

[[Category:Machine Learning]]
[[Category:Neuroscience]]
[[Category:Complex Systems]]
[[Category:Self-organization]]