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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.
Overview. LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning -based approach. It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN). LVQ was invented by Teuvo Kohonen. [1]
Self-organizing map [ edit ] The self-organizing map (SOM, also called Kohonen map ) and its probabilistic variant generative topographic mapping (GTM) use a point representation in the embedded space to form a latent variable model based on a non-linear mapping from the embedded space to the high-dimensional space. [ 6 ]
U-matrix. The U-matrix ( unified distance matrix) is a representation of a self-organizing map (SOM) where the Euclidean distance between the codebook vectors of neighboring neurons is depicted in a grayscale image. This image is used to visualize the data in a high-dimensional space using a 2D image. [ 1]
In artificial neural networks, a hybrid Kohonen self-organizing map is a type of self-organizing map (SOM) named for the Finnish professor Teuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer. The output from the Kohonen layer, which is the winning neuron, feeds into a hidden ...
A growing self-organizing map (GSOM) is a growing variant of a self-organizing map (SOM). The GSOM was developed to address the issue of identifying a suitable map size in the SOM. It starts with a minimal number of nodes (usually 4) and grows new nodes on the boundary based on a heuristic. By using the value called Spread Factor (SF), the data ...
Generative topographic map. Generative topographic map ( GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking ...
Elastic map is represented by a set of nodes in the same space. Each datapoint has a host node, namely the closest node (if there are several closest nodes then one takes the node with the smallest number). The data set is divided into classes . The approximation energy D is the distortion. ,