Principal Component Analysis using Constructive Neural Networks
Paper in proceeding, 2007

In this paper, a new constructive auto-associative neural network performing nonlinear principal component analysis is presented. The developed constructive neural network maps the data nonlinearly into its principal components and preserves the order of principal components at the same time. The weights of the neural network are trained by a combination of back propagation (BP) and genetic algorithm (GA) which accelerates the training process by preventing local minima. The performance of the proposed method was evaluated by means of two different experiments that illustrated its efficiency.

Neural nets

Genetic algorithms

Principal component analysis


Behrooz Makki

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

Seyedali Seyedsalehi

Mona Noori-Hosseini

Nasser Sadati

International Joint Conference on Neural Networks

1098-7576 (ISSN)

Vol. 1 1 558-562
978-1-4244-1380-5 (ISBN)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering



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8/7/2018 1