swMATH ID: 39434
Software Authors: Bishop, CM; Svensén, M.; Williams, CK
Description: GTM: The Generative Topographic Mapping. Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.
Homepage: https://www.microsoft.com/en-us/research/wp-content/uploads/1998/01/bishop-gtm-ncomp-98.pdf
Related Software: UCI-ml; ElemStatLearn; KernSmooth; darch; t-SNE; SOM; COIL-20; PRMLT; PyTorch; word2vec; ClusVis; CoModes; clustMD; Rmixmod; funHDDC; funFEM; PCAmixdata; FactoMineR; fda (R); mclust
Referenced in: 32 Publications

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