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Principal component analysis using constructive neural networks

Makki, B ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/IJCNN.2007.4371017
  3. Publisher: 2007
  4. Abstract:
  5. 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. ©2007 IEEE
  6. Keywords:
  7. Artificial intelligence ; Back propagation ; Back propagation algorithms ; Computer networks ; Diesel engines ; Financial data processing ; Genetic algorithms ; Image classification ; Nonlinear network analysis ; Vegetation ; Principal components ; Neural networks
  8. Source: 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, 12 August 2007 through 17 August 2007 ; 2007 , Pages 558-562 ; 10987576 (ISSN) ; 142441380X (ISBN); 9781424413805 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4371017