A Neural Network Learning Algorithm for PCA and MCA

王哲; 李衍达; 罗发龙

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A Neural Network Learning Algorithm for PCA and MCA

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- A Neural Network Learning Algorithm for PCA and MCA
- Acta Electronica Sinica Issue 4, (1996)
- 作者机构：
  
  清华大学自动化系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：1996
- 稿件说明：
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[1]王哲,李衍达,罗发龙.一种用于PCA与MCA的神经网络学习算法[J].电子学报,1996(04):12-16.

王哲, 李衍达, 罗发龙. A Neural Network Learning Algorithm for PCA and MCA[J]. Acta Electronica Sinica, 1996, (4).
[1]王哲,李衍达,罗发龙.一种用于PCA与MCA的神经网络学习算法[J].电子学报,1996(04):12-16. DOI：

王哲, 李衍达, 罗发龙. A Neural Network Learning Algorithm for PCA and MCA[J]. Acta Electronica Sinica, 1996, (4). DOI：

摘要

主元分析（ＰＣＡ）和次元分析（ＭＣＡ）是用于特征提取、数据压缩、频率估计、曲线拟合等信号处理的基本技术．以神经网络来实现ＰＣＡ和ＭＣＡ是当今研究的一大热点，相关矩阵Ｒ的特征值重数不为１时的主、次元分析则是其中一大难题．本文提出了一种新的学习算法，使得在输入数据的相关矩阵含多重特征值时，网络权重矢量亦收敛于相关矩阵的单位正交特征矢量

Abstract

Principal Component Analysis（PCA） and Minor Component Analysis（MCA） are essential techniques in signal processing such as feature extraction

data compression

frequency estimation and curve fitting.Recently

there has been much interest in the connection between PCA

MCA and neural networks.It is a difficult problem in PCA and MCA when eigenvalues of covariance matrix R are not distinct.A new learning algorithm that the weight vectors will converge to orthonormal eigenvectors is proposed in this paper to solve the problem.

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