最小残差熵与最小均方差的主元网络及其比较

Guo, Zhenhua and Yue, Hong and Wang, Hong (2005) 最小残差熵与最小均方差的主元网络及其比较. Pattern Recognition and Artificial Intelligence, 18 (1). pp. 96-102. (https://doi.org/10.3969/j.issn.1003-6059.2005.01.0...)

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Abstract

Principal component analysis and neural network combining PCA provides an adaptive neural network parallel line of the main element and master spatial analysis techniques, but the data for non-Gaussian stochastic systems based on the minimum mean square error of the reconstructed PCA extracted . the main element direction than the direction of maximizing the information paper first presents a minimum mean square error based on self-association of the main element network analysis of variance reconstruction of its best properties of both IT and non-maximizing features; then presented with minimal residual Poor information entropy learning objectives PCA neural network and gives the approximate calculation method residual entropy network output and network learning methods; finally analyzed in Gaussian random distribution system, the minimum residual entropy and minimum mean square error of reconstruction The results are consistent.

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