高维少样本数据的特征压缩
Computer Engineering and Applications(2009)
Abstract
针对一类高维少样本数据的特点,给出了广义小样本概念,对广义小样本进行信息特征压缩:特征提取(降维)和特征选择(选维).首先介绍基于主成分分析(PCA)的无监督与基于偏最小二乘(PLS)的有监督的特征提取方法;其次通过分析第一成分结构,提出基于PCA与PLS的新的全局特征选择方法,并进一步提出基于PLS的递归特征排除法(PLS-RFE);最后针对MIT AML/ALL的分类问题,实现基于PCA与PLS的特征选择和特征提取,以及PLS-RFE特征选择与比较,达到广义小样本信息特征压缩的目的.
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Key words
feature selection,feature extraction,Partial Least Squares(PLS),generalized small sample,Principal Component Analysis(PCA)
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