摘要
主成分分析是一种常用的特征选择算法,经典方法是计算各个特征之间的相关,但是相关无法评估变量间的非线性关系.互信息可用于衡量两个变量间相互依赖的强弱程度,且不局限于线性相关,鉴于此,提出一种基于互信息的主成分分析特征选择算法.该算法计算特征间的互信息,以互信息矩阵的特征值作为评价准则确定主成分的个数,并衡量主成分分析特征选择的效果.通过实例对所提出方法和传统主成分分析方法进行比较,并以神经网络为分类器分析分类效果.
@@@@Principal component analysis(PCA) is a common method for feature selection. The classical procedure to obtain principal components is calculating the correlation matrix between features. However, the correlation cannot reflect the nonlinear relationship. Mutual information measures the interdependence strength between variables which are not limited to the linear correlation. PCA based on mutual information(MIPCA) for feature selection is presented. The algorithm calculates the mutual information matrix and extracts the eigenvalues as the criteria to determine the number of principal components and assess the effect of feature selection. Finally, the proposed algorithm is compared with PCA by cases, and the efficiency of classification is tested by neuron network.
出处
《控制与决策》
EI
CSCD
北大核心
2013年第6期915-919,共5页
Control and Decision
基金
国家自然科学基金青年科学基金项目(11104316)
上海市自然科学基金项目(11ZR1446000)
关键词
互信息
主成分分析
特征选择
mutual information
principal component analysis
feature selection