@article{331c3fab274448d9bd20ee6e3a290938, title = "GTM: The generative topographic mapping", abstract = "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 transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization 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 multiphase oil pipeline.", author = "Bishop, {Christopher M} and Markus Svens{\'e}n and Williams, {Christopher KI}", year = "1998", month = jan, doi = "10.1162/089976698300017953", language = "English", volume = "10", pages = "215--234", journal = "Neural Computation", issn = "0899-7667", publisher = "MIT Press", number = "1", }