Magnification factors specify the extent to which the area of a small patch of the latent
(orfeature') space of a topographic mapping is magnified on projection to the data space …

The generative topographic mapping (GTM) algorithm of CM Bishop et al.(1996) has been
introduced as a principled alternative to the self-organizing map (SOM). As well as avoiding …

The generative topographic mapping (GTM) model was introduced by Bishop et al.(1998,
Neural Comput. 10 (1), 215–234) as a probabilistic re-formulation of the self-organizing map …

A multilayer hierarchical self-organizing map (HSOM) is discussed as an unsupervised
clustering method. The HSOM is shown to form arbitrarily complex clusters, in analogy with …

This paper concentrates on the following issues:(1) discussion on what kind of mapping is
produced by the SOM algorithm;(2) introduction of a quantitative measure of continuity for …

We create a new form of topographic map which is based on a nonlinear mapping of a
space of latent points. The mapping of these latent points into data space creates centres …

We offer three algorithms for the generation of topographic mappings to the practitioner of
unsupervised data analysis. The algorithms are each based on the minimization of a cost …

An important technique for exploratory data analysis is to form a mapping from the high-
dimensional data space to a low-dimensional representation space such that …

We review a new form of self-organizing map which is based on a nonlinear projection of
latent points into data space, identical to that performed in the Generative Topographic …

Several methods to visualize clusters in high-dimensional data sets using the Self-
Organizing Map (SOM) have been proposed. However, most of these methods only focus on …