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Principal Component Analysis using Constructive Neural Networks

Behrooz Makki (Institutionen för signaler och system, Kommunikationssystem) ; Seyedali Seyedsalehi ; Mona Noori Hosseini ; Nasser Sadati
International Joint Conference on Neural Networks (1098-7576). Vol. 1 (2007), 1, p. 558-562.
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Genetic algorithms; Neural nets; Principal component analysis



Denna post skapades 2009-02-25. Senast ändrad 2010-09-09.
CPL Pubid: 90435

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem

Ämnesområden

Elektroteknik och elektronik

Chalmers infrastruktur