用于非高斯系统降维的最小残差熵主元网络

Guo, Zhenhua and Yue, Hong and Wang, Hong (2005) 用于非高斯系统降维的最小残差熵主元网络. Computer Simulation, 22 (11). pp. 91-94. ISSN 1006-9348 (https://doi.org/10.3969/j.issn.1006-9348.2005.11.0...)

Full text not available in this repository.Request a copy

Abstract

Based on the minimum mean square error of principal component analysis and principal component neural network is an effective dimensionality reduction of multivariate statistical techniques, they extract the main element contains the system maximum variance approximation model non-Gaussian stochastic systems should contain maximum entropy system, but contains the maximum variance does not necessarily contain the maximum entropy. This paper presents a minimal residual entropy for the general index nonlinear principal component neural network model, and gives an approximate calculation method based on Parzen window density function estimation of entropy and network learning algorithm then analyzed from the perspective of information theory, the Gaussian random system based on minimum residual entropy and minimum mean square as an indicator of the primary element network learning outcomes consistent. Finally, simulation effectiveness of the method, and with comparative analysis based on minimum mean square error calculation principal component analysis and principal component neural network method.

ORCID iDs

Guo, Zhenhua, Yue, Hong ORCID logoORCID: https://orcid.org/0000-0003-2072-6223 and Wang, Hong;
CORE (COnnecting REpositories)