Abstract
The minor component is the eigenvector associated with the smallest eigenvalue of the covariance matrix of the input data. The minor component analysis (MCA) is a statistical method for extracting the minor component. Many neural networks have been proposed to solve MCA. However, there exists the problem of the divergence of the norm of the weight vector in these neural networks. In this paper, a modification to the well known MCA EXIN algorithm is presented by adjusting the learning rate. The modified MCA EXIN algorithm can guarantee that the norm of the weight vector of the neural network converges to a constant. Mathematical proofs and simulation results are given to show the convergence of the algorithm.
This work was supported by National Science Foundation of China under Grant 60471055 and Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20040614017.
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© 2005 Springer-Verlag Berlin Heidelberg
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Peng, D., Yi, Z., Xiang, X. (2005). A Modified MCA EXIN Algorithm and Its Convergence Analysis. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_165
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DOI: https://doi.org/10.1007/11427391_165
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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