self-organizing map
In plain wordsA neural network that arranges data on a flat grid so that similar items end up in neighboring cells, producing a map of how the data is organized.
An unsupervisedunsupervised learning, n.: Machine learning without answer keys: the computer looks at unlabeled data and finds structure on its own, such as groups of similar customers or the main ways in which data vary. neural network whose units occupy fixed places on a grid, usually two-dimensional, and are trained until neighbors on the grid stand for similar inputs, so that the grid becomes a flat chart of high-dimensional data that keeps similar items together. Each unit holds a prototype vector; for each training input the best-matching unit is found, and it and its grid neighbors are moved towards the input, with the neighborhood radius and learning rate shrinking over time. The result is a discrete, ordered map useful for visualizing clustersclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size., exploring high-dimensional data and vector quantization. It is a form of competitive learning, akin to k-means clusteringk-means clustering, n.: A simple way to split data into a chosen number of groups: guess group centers, assign every point to its nearest center, move each center to the middle of its points, and repeat. with a neighborhood constraint, and was inspired by topographic maps in the cerebral cortex. Largely superseded for visualization by t-SNEt-SNE, n.: A technique for drawing data that have many measurements per item as a flat map in which similar items land close together, widely used to look for groups in AI models' internal data or in genetic data. and UMAPUMAP, n.: A fast technique for turning data with many measurements per item into a flat map that keeps similar items together; often preferred to older methods because it is quicker and copes with more data., it remains in use in some scientific and engineering fields.
On the self-organizing map, the faulty sensor readings cluster in the top-right corner.
Origin
Introduced by Finnish engineer Teuvo Kohonen in 'Self-Organized Formation of Topologically Correct Feature Maps' (Biological Cybernetics, 1982), building on earlier models of cortical self-organization such as Christoph von der Malsburg's (1973).
Related
- unsupervised learningunsupervised learning, n.: Machine learning without answer keys: the computer looks at unlabeled data and finds structure on its own, such as groups of similar customers or the main ways in which data vary.
- clusteringclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size.
- k-means clusteringk-means clustering, n.: A simple way to split data into a chosen number of groups: guess group centers, assign every point to its nearest center, move each center to the middle of its points, and repeat.
- dimensionality reductiondimensionality reduction, n.: Squeezing data that has many measurements per item into far fewer numbers while keeping the patterns that matter, so it is easier to visualize, store or learn from.
- t-SNEt-SNE, n.: A technique for drawing data that have many measurements per item as a flat map in which similar items land close together, widely used to look for groups in AI models' internal data or in genetic data.
- UMAPUMAP, n.: A fast technique for turning data with many measurements per item into a flat map that keeps similar items together; often preferred to older methods because it is quicker and copes with more data.
Sources
- Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43, 59–69.
Mentioned in
Teuvo KohonenTeuvo Kohonen, prop. n.: A Finnish engineer who invented the self-organizing map, a neural network that arranges complex data on a simple grid so that similar items end up next to each other.