Tight clusters and smooth manifolds with the harmonic topographic map

Authors:
Marian Peña;Colin Fyfe
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland;Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland
Venue:
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Year:
2005

Citing 3
Cited 1

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ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

Faster clustering of complex data with the generalised harmonic topographic mapping(G-HaToM)

AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications

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Abstract

We review a new form of self-organizing map introduced in [5] which is based on a non-linear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. We discuss a refinement of that mapping (M-HaToM) and show on real and artificial data how it both finds the true manifold on which a data set lies and also clusters data more tightly than the previous algorithm (D-HaToM).