互信息诱导子空间集成偏最小二乘在近红外光谱定量校正中的应用  

Ensemble Partial Least Squares Algorithm in Mutual Information-Induced Subspace for Near-infrared Quantitative Calibration

作　　者：谭超[1,2] 覃鑫[1] 李梦龙[3] 

机构地区：[1]宜宾学院化学与化工系,宜宾644007 [2]宜宾学院计算物理重点实验室,宜宾644007 [3]四川大学化学学院,成都610064

出　　处：《分析化学》2009年第12期1834-1838,共5页Chinese Journal of Analytical Chemistry

基　　金：四川省青年科技基金(No.09ZQ026-006);宜宾学院博士科研启动基金(No.2008B06)资助项目

摘　　要：在集成框架下,提出了一种联合自助采样和基于互信息变量选择的子空间回归集成偏最小二乘算法MISEPLS。此算法的核心是通过训练集自助采样和随后计算互信息的方式来引入成员模型的差异性。由于互信息量小于一个特定阈值的变量被淘汰,每个成员模型在原始变量的一个子空间得到训练。模型融合考虑了简单平均和加权平均两种方式。通过两个近红外光谱定量校正实验,与建立单模型的全谱偏最小二乘算法(PLS)和基于互信息变量选择的偏最小二乘算法(MIPLS)进行了比较。结果表明,在不增加模型复杂度的情况下,MISEPLS能建立起更精确、更稳健的校正模型。In the framework of ensemble, a partial least squares （PLS） regression ensemble algorithm in subspace（MIESPLS） , which is the combination of bootstrap and variable selection based on mutual information （MI） , was proposed. The key of the proposed algorithm is to introduce the diversity of member models by bootstrap re-sampling on the training set and the subsequent MI calculation. Each time, those variables whose MI are lower than a defined threshold are first eliminated; then, a member model can be trained on a smaller subspace of original spectral variables. Two kinds of model fusion strategies, i. e. , simple average fusion （SAF） and weighted average fusion（WAF）, were adopted and compared. By two experiments concerning quantitative application of near-infrared（NIR） spectroscopy, MISEPLS is confirmed to be superior to the full-spectrum PLS and MIPLS method, i. e. , PLS combined with MI-induced variable selection. The proposed MISEPLS can produce a more accurate and robust calibration model, but without increasing the complexity.

关 键 词：互信息 子空间 集成 校正 近红外光谱 

分 类 号：O657.33[理学—分析化学] TQ461[理学—化学]