Growing Self-Organizing Networks (GSOM) are a type of unsupervised artificial neural network used for learning and visualization of high-dimensional data. GSOMs are based on a mesh or grid structure, where each node represents an input region in the feature space of the data.
The GSOM learning process is divided into two main phases: a growth phase and a pruning phase. In the growth phase, nodes are dynamically added to the network as needed to accommodate the distribution of data. In the pruning phase, unnecessary nodes are removed, keeping only the nodes that are relevant to the data representation.
GSOMs are often used for visualization and exploration of large, high-dimensional data sets. The mesh structure of the network allows for a two-dimensional representation of the data, which facilitates the identification of patterns and relationships between the data. In addition, GSOMs have the ability to adapt to new data, making them useful for real-time applications.
Reference: "A growing self-organizing network for reconstructing curves and surfaces", Piastra, Marco, in Neural Networks, 2009. IJCNN 2009. International Joint Conference on, IEEE, 2009, pp. 2533-2540.
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