Abstract: Principal Component Analysis（PCA）, a conventional dimension reduction method based on unsupervised learning, extracts components effectively, irrespective of the class information. As the samples have a high-correlation or multi-correlation, PCA method is invalid. Nonlinear iterative Partial Least Square（PLS） using supervised learning is proposed, wherein a technique for modeling a relationship between a set of input variables and output variables, while maintaining most of the information in the input variables, and then extract the discrimination of classes features, until convergence of latent vector. Experimental results on the ORL face database and the Yale face database demonstrate the effectiveness of the presented scheme.

Key words: nonlinear iterative, Partial Least Square（PLS）, face recognition

摘要： 主成分分析（PCA）是一种无监督的线性降维方法，能有效地提取模式的类内特征，当样本之间出现高度相关性或多重相关性时，PCA提取的主成分解释能力不够。鉴于PCA的缺点，采用一种有监督的鉴别特征提取法——偏最小二乘（PLS），在保留输入变量的最大信息条件下，先在输入和输出变量组中建立模型，再用非线性迭代法提取类间特征，直至隐变量收敛。在ORL人脸库和Yale人脸库中实验结果表明，该算法具有有效性。

关键词: 非线性迭代, 偏最小二乘, 人脸识别