The Block Generative Topographic Mapping
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Dimension reduction and visualization of large high-dimensional data via interpolation
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
The semantics of clustering: analysis of user-generated spatializations of text documents
Proceedings of the International Working Conference on Advanced Visual Interfaces
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We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data sparseness. The associated parameter estimation algorithm scales linearly with the number of non-zero entries in the observations while still learning a truly nonlinear generative mapping. The latent variables of the model lie in a 2D space that can be used for visualisation. We discuss related work and we provide experimental results on text based documents visualisation as well as the exploratory analysis of web navigation sequences.