A principal components analysis self-organizing neural network model and computational experiment

DOI

10.2991/iccia.2012.209How to use a DOI?

Keywords

Self-organization, Principal component analysis, Competitive learning, Pattern recognition

Abstract

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jifu Nong
PY  - 2014/05
DA  - 2014/05
TI  - A principal components analysis self-organizing neural network model and computational experiment
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 859
EP  - 862
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.209
DO  - 10.2991/iccia.2012.209
ID  - Nong2014/05
ER  -