GTM: the generative topographic mapping
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
Self-Organizing Maps
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Topographic mapping of large dissimilarity data sets
Neural Computation
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The generative topographic mapping (GTM) models data by a mixture of Gaussians induced by a low-dimensional lattice of latent points in low dimensional space. Using back-projection, topographic mapping and visualization can be achieved. The original GTM has been proposed for vectorial data only and, thus, cannot directly be used to visualize data given by pairwise dissimilarities only. In this contribution, we consider an extension of GTM to dissimilarity data. The method can be seen as a direct pendant to GTM if the dissimilarity matrix can be embedded in Euclidean space while constituting a model in pseudo-Euclidean space, otherwise. We compare this visualization method to recent alternative visualization tools.