摘要
应用近红外透射光谱,实施规范变量提取-线性判别分析(CVA-LDA)技术,对其分属不同药物剂量类型进行鉴别。针对药物近红外透射光谱频道变量个数多,彼此间存在严重的复共线性,富有冗余信息,样本类(模式)内的离散度矩阵为奇异,由传统CVA方法提取规范变量的计算不稳健,提出了改进的规范变量分析ICVA方法。它通过嵌入偏最小二乘(PLS)算法,完成规范权矢量的稳健估计,进而用于提取出若干个规范变量。而后,基于规范变量张成的低维空间,构造样本类别的线性判别函数,负责各样本个体类别的判定。实验结果表明,改进的ICVA-LDA方法,克服了LDA对高维小样本数据建模的局限性,模型的判别能力明显优于其他方法。
A novel classifier was constructed in the present paper by combination of an improved canonical variates analysis （ICVA） with Fish linear discriminant analysis （LDA）. The resulting discrimination model based on this proposed approach （ICVALDA） was divided into two parts： the inner part that estimated the robust weight vector of canonical variates by linear partial least square algorithm and the outer part that built the LDA discrimination model by making use of the extracted canonical variares. The method utilized partial least squares regression as an engine for solving an eigenvector problem involving singular covariance matrices and the canonical variates were more relevant for discriminative purposes. Thus, the weight vectors found in the modified CVA method not only possessed the same properties as weight vectors of the standard CVA method, but also forced the discriminative information into the first fewer of canonical variates. The improved discrimination model was more concise and efficient in dealing with the problem of the effect sensitivity and numerous predictor variables With serious multicollinearity in the spectra data. Furthermore, in ICVA-LDA the interpretation could be performed with respect to the oiginal high-dimensional data space. Finally, application to a four-group problem with near-infrared transmittance spectroscopy data consisting of 310 sampies and 404 variables of the proposed ICVA-LDA approach was presented with comparison to the LDA combined with principal component analysis （PCA-LDA） and standard CVA-LDA methods. All the three discrimination models were validated using fivefold segmented cross-validation. The result demonstrates that the limitations of LDA were overcome with PLS algorithm and then the classification performance of LDA was improved by ICVA. This proposed approach can also be widely used in other fields for classification and discrimination of small samples and collinear data.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第3期624-628,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(20276063)
浙江省自然科学基金项目(Y406053)资助
关键词
规范变量分析
线性判别分析
偏最小二乘
模式分类
近红外透射光谱
Canonical variates analysis Linear discriminate analysis, Partial least squares Pattern classification
Near-infrared transmittance spectroscopy
