Visualizing Dissimilarity Data Using Generative Topographic Mapping

Abstract

AbstractThe 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.KeywordsTarget VectorDissimilarity MatrixLocally Linear EmbeddingLaplacian EigenmapsGenerative Topographic MappingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.