计算机工程2011，Vol.37Issue(22)：174-175,178,3.
基于核函数的PCA-L1算法
PCA-L1 Algorithm Based on Kernel Function
摘要
Abstract
Because of using L2 norm, Principal Component Analysis(PCA) method is sensitive to outliers. So this paper proposes a PCA method based on kernel function and LI norm. It maps original data to kernel space to get a kernel matrix, and utilizes kernel function and LI norm to minimize the distance function. Experimental result shows that the algorithm is invariant to rotations and robust to outliers and nonlinear problems, and it has higher correct recognition rate.关键词
PCA-L1算法/L1范数/核主成分分析/核函数/人脸识别Key words
PCA-L1 algorithm/ LI norm/ Kernel Principal Component Analysis(KPCA)/ kernel function/ face recognition分类
信息技术与安全科学引用本文复制引用
李勇,梁志贞,夏士雄..基于核函数的PCA-L1算法[J].计算机工程,2011,37(22):174-175,178,3.基金项目
国家自然科学基金资助项目(61003169) （61003169）
