Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
Data Mining and Knowledge Discovery
The topographic product of experts
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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Topographic maps are low dimensional maps which retain some topology of the original dataset. Many topographic mappings suffer from having the dimensionality of the map determined beforehand which is certain to be inappropriate for some data sets. In this paper, we develop a method of investigating a data set enabling the local dimensionality of the map to change. Our model of the data allows us to traverse the main manifold on which the data lies while giving information about the local dimensionality of the data around this main manifold.