关键词: 非线性迭代, 偏最小二乘, 人脸识别

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