GTM
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.
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References in zbMATH (referenced in 57 articles )
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Sorted by year (- Biernacki, Christophe; Marbac, Matthieu; Vandewalle, Vincent: Gaussian-based visualization of Gaussian and non-Gaussian-based clustering (2021)
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- Hua, Hao: Image and geometry processing with oriented and scalable map (2016)
- Iwasaki, Tohru; Furukawa, Tetsuo: Tensor SOM and tensor GTM: nonlinear tensor analysis by topographic mappings (2016)
- Liu, Binghui; Shen, Xiaotong; Pan, Wei: Nonlinear joint latent variable models and integrative tumor subtype discovery (2016)
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- Astudillo, César A.; Oommen, B. John: Topology-oriented self-organizing maps: a survey (2014) ioport
- Feng, Wenyue; Yang, Zhouwang; Deng, Jiansong: Moving multiple curves/surfaces approximation of mixed point clouds (2014)
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- Zhang, Xiaofeng; Cheung, William K.; Li, C. H.: Learning latent variable models from distributed and abstracted data (2011) ioport
- Zhou, Tianyi; Tao, Dacheng; Wu, Xindong: Manifold elastic net: a unified framework for sparse dimension reduction (2011)