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An adaptive robust PCA neural network

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3 Author(s)
Wang Song ; Dept. of Autom., Tsinghua Univ., Beijing, China ; Liang Yilong ; Ma Feng

We find one way to improve the robustness of principal component analysis (PCA) based on a reconstruction error model. First, we discuss and compare the methods to analyze the robustness of the PCA algorithm. A new adaptive algorithm of robust PCA based on the structure of a single-layer neural network (NN) is developed with the modification of the cost function which can be acquired through modeling of the error function. The new nonlinear robust PCA algorithm can reduce the effects of outliers on the accuracy and convergence of the PCA algorithm through proper processing of them. Simple comparison simulations are designed for verify the theoretical results

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:3 )

Date of Conference:

4-9 May 1998

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