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

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:
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Year:
2005

Citing 3
Cited 0

Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tight clusters and smooth manifolds with the harmonic topographic map

SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
The topographic product of experts

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

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we explain the Generalised Harmonic topographic Map (G-HaToM), an extension of the Harmonic Topographic map [3] and [4]. This algorithm extends the mapping from data space to latent space using the pth power of the L2 distance, where the second power is the former version of the topographic mapping, HaToM. This generalization allows the mapping of more difficult data, reducing at the same time the computational cost of the mapping for the data already clustered by the original HaToM.