统计模式识别中的维数削减与低损降维

较为全面地回顾了统计模式识别中常用的一些特征选择、特征提取等主流特征降维方法,介绍了它们各自的特点及其适用范围,在此基础上,提出了一种新的基于最优分类器——贝叶斯分类器的可用于自动文本分类及其它大样本模式分类的特征选择方法——低损降维.在标准数据集Reuters-21578上进行的仿真实验结果表明,与互信息、χ^2统计量以及文档频率这三种主流文本特征选择方法相比,低损降维的降维效果与互信息、χ^2统计量相当,而优于文档频率.

Dimensionality Reduction in Statistical Pattern Recognition and Low Loss Dimensionality Reduction

First,authors review the prevailing feature selection methods such as Exhaustive Search,Genetic Algorithm,Sequential Forward Floating Selection,and Best Individual Features,and feature extraction approaches such as Principal Component Analysis,Fisher Discriminant Analysis,and Projection Pursuit for feature space dimensionality reduction in statistical pattern recognition.Second,authors discuss the characteristics and the applicable domains of all these techniques.Third,authors propose a novel feature selection method based on so-called optimal classifier,Bayesian classifier.The new feature selection method,i.e.the low loss dimensionality reduction(LLDR),is applied in automatic text categorization and compared with the prevailing feature selection methods such as Mutual Information(MI),Chi-square Statistic(CHI),and Document Frequency(DF) in automatic text categorization.Experimental results performed on the well known dataset Reuters-21578 show that the ability for dimensionality reduction of LLDR compared with those of MI and CHI,and higher than that of DF.Considering that LLDR is more computational efficient than MI and CHI,LLDR is a promising feature selection method for automatic text categorization.