摘要
采用数据样本间的相关性作为相似性度量,并引入样本的类信息,提出一种新的降维方法,即伪主成分分析(Pseudo-PCA),该方法尽可能地保持原样本的变化信息,同时又使得降维后的同类数据样本尽可能保持相似。此外,将这种思想方法成功推广到近年来提出的2DPCA,MatPCA和(2D)2PCA。在ORL,Yale和AR等人脸数据集上的实验表明,该类方法的识别率高于相应的基于欧氏距离的PCA,2DPCA,M atPCA和(2D)2PCA等方法。
A new dimensionality reduction method, called the pseudo-PCA, is proposed, in which the correlation between the samples is taken as the similarity metric. Meanwhile the class information of the samples is incorporated. Pseudo-PCA can preserve the variation information of the samples and enable the data within the same class to be similar to each other. Moreover, the idea of pseudo PCA is generalized to the recently proposed 2DPCA, MatPCA and （2D）^2PCA. Experimental results on ORI., Yale and AR face datasets show that pseudo -PCA, -2DPCA, -MatPCA and -（2D）^2PCA based on the correlation metric outperform PCA, 2DPCA, MatPCA and （2D）^2PCA based on the Euclidian distance.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2006年第6期732-736,共5页
Journal of Nanjing University of Aeronautics & Astronautics
基金
江苏省自然科学基金(BK2005122)资助项目
关键词
主成分分析
相似性度量
类信息
欧氏距离
人脸识别
principal component analysis （PCA）
similarity metric
class information
Euclidian distance
face recognition
