Intrinsic dimensionality maps with the PCASOM

Authors:
Ezequiel López-Rubio;Juan Miguel Ortiz-de-Lazcano-Lobato;María del Carmen Vargas-González;José Miguel López-Rubio
Affiliations:
School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 6
Cited 0

An Evaluation of Intrinsic Dimensionality Estimators

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension reduction by local principal component analysis

Neural Computation
A unifying review of linear Gaussian models

Neural Computation
Mixtures of probabilistic principal component analyzers

Neural Computation
Principal components analysis competitive learning

Neural Computation
An Algorithm for Finding Intrinsic Dimensionality of Data

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

The PCASOM is a novel self-organizing neural model that performs Principal Components Analysis (PCA). It is also related to the ASSOM network, but its training equations are simpler. The PCASOM has the ability to learn self-organizing maps of the means and correlations of complex input distributions. Here we propose a method to extend this capability to build intrinsic dimensionality maps. These maps model the underlaying structure of the input. Experimental results are reported, which show the self-organizing map formation performed by the proposed network.