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. Copyright c MIT Press (1998). 

• Type: Journal
• Year: 1998
• Where: NECO
• Authors: Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
• Keywords: Generative Topographic Mapping, Latent Space, Latent Variable Model, NECO 1998