摘要: 主成分分析方法由于使用了L2范数,因此对异常值较敏感。针对该问题,提出一种基于核函数的L1范数主成分分析方法。运用核函数将原始数据映射到核空间中得到核矩阵,再利用L1范数使距离函数达到最小。实验结果表明,该算法具有旋转不变性,对异常值和非线性问题具有稳定性,且正确识别率较高。
关键词:
PCA-L1算法,
L1范数,
核主成分分析,
核函数,
人脸识别
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 L1 norm. It maps original data to kernel space to get a kernel matrix, and utilizes kernel function and L1 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.
Key words:
PCA-L1 algorithm,
L1 norm,
Kernel Principal Component Analysis(KPCA),
kernel function,
face recognition
中图分类号:
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.
LI Yong, LIANG Zhi-Zhen, JIA Shi-Xiong. PCA-L1 Algorithm Based on Kernel Function[J]. Computer Engineering, 2011, 37(22): 174-175.