1.陕西工业职业技术学院 信息工程学院,陕西 咸阳 712000; 2.西北农林科技大学 机械与电子工程学院,陕西 杨凌 712100; 3.西北农林科技大学 信息工程学院,陕西 杨凌 712100

降维算法; 图像处理; 主成分析

L-PCA-based dimensionality reduction algorithm for high dimension images
LI Long-long1,2,HE Dong-jian2,WANG Mei-li3

(1.College of Information Engineering,Shaanxi Polytechnic Institute,Xianyang 712000,China; 2.College of Mechanical & Electronic Engineering,Northwest A&F University,Yangling 712100,China; 3.College of Information Engineering,Northwest A&F University,Yangling 712100,China)

dimensionality reduction algorithm; image processing; PCA

DOI: 10.13800/j.cnki.xakjdxxb.2017.0621



Based on the idea of global linear dimensionality reduction algorithm named PCA from classical convex clustering algorithm and LDA,an improved PCA method called L-PCA was introduced.The algorithm retained the covariance structure of the original samples,chose the most important principal component from transformation matrix for empowerment.By adjusting the discrete matrixes for inner-class and inter-class,the distances in the same class were minimized and the ones for inner-class were maximized to search for a suitable mapping subspace to separate the data between different categories.The results show that L-PCA performs well regarding generalization errors of 1-NN classifiers,accuracy and continuity.