Abstract
Principal component analysis (PCA) by neural networks is one of the most frequently used feature extracting methods. To process huge data sets, many learning algorithms based on neural networks for PCA have been proposed. However, traditional algorithms are not globally convergent. In this paper, a new PCA learning algorithm based on cascade recursive least square (CRLS) neural network is proposed. This algorithm can guarantee the network weight vector converges to an eigenvector associated with the largest eigenvalue of the input covariance matrix globally. A rigorous mathematical proof is given. Simulation results show the effectiveness of the algorithm.
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Acknowledgements
This work is supported in part by the National Science Foundation of China under grant number A0324638 and Youth Science and Technology Foundation of UESTC YF020801.
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Ye, M., Yi, Z. & Lv, J. A globally convergent learning algorithm for PCA neural networks. Neural Comput & Applic 14, 18–24 (2005). https://doi.org/10.1007/s00521-004-0435-y
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DOI: https://doi.org/10.1007/s00521-004-0435-y