Neural Network Learning for Principal Component Analysis： A Multistage Decomposition Approach  

作　　者：FENGDazheng ZHANGXianda BAOZheng 

机构地区：[1]KeyLaboratoryforRadarSignalProcessing,XidianUniversity,Xi＇an710071,China [2]KeyLaboratoryforRadarSignalProcessing,XidianUniversity,Xi＇an710071,China

出　　处：《Chinese Journal of Electronics》2004年第1期1-7,共7页电子学报（英文版）

摘　　要：This paper presents a novel neural network model for finding the principal components of an Ndimensional data stream. This neural network consists of r (≤N) neurons, where the i-th neuron has only N - i+1 weights and an N- i+1 dimensional input vector, while each neuron in most of the relative classical neural networks includes N weights and an N dimensional input vector. All the neurons are trained by the NIC algorithm under the single component case^[7] so as to get a series of dimension-reducing principal components in which the dimension number of the i-th principal component is N- i+1. In multistage dimension-reducing processing, the weight vector of i-th neuron is always orthogonal to the subspace constructed from the weight vectors of the first i-1 neurons. By systematic reconstruction technique, wecan recover all the principal components from a series of dimension-reducing ones. Its remarkable advantage is that its computational efficiency of the neural network learning based on the Novel information criterion (NIC) is improved and the weight storage is reduced, by the multistage dimension-reducing processing (multistage decomposition)for the covariance matrix or the input vector sequence. In addition, we study several important properties of the NIC learning algorithm.

关 键 词：神经网络 主成分分析 多级分解 NIC 性能 n维数据流 信息准则 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP183[自动化与计算机技术—计算机科学与技术]