Abstract
Minor component analysis(MCA) by neural network is endowed with a stochastic discrete-time(SDT) weight vector learning algorithm. It is very difficult to study such algorithm directly. Previous theoretical results on the SDT algorithm are based on its deterministic continuous-time(DCT) ODE asymptotic approximation. However, in general, they are not equivalent at all. Since the behavior of the conditional expectation of the weight vector can be studied by the deterministic discrete-time(DDT) algorithm, it is reasonable to study the SDT algorithm by its DDT algorithm indirectly. By studying the DDT algorithms, we can prove that some of previous MCA neural networks are globally convergent if the learning rate is a variable.
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Ye, M., Yi, Z. (2004). On the Discrete Time Dynamics of the MCA Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_134
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DOI: https://doi.org/10.1007/978-3-540-28647-9_134
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