[1]李龙龙,何东健,王美丽.L-PCA算法下的高维图像降维算法研究[J].西安科技大学学报,2017,(06):906-911.[doi:10.13800/j.cnki.xakjdxxb.2017.0621 ]
 LI Long-long,HE Dong-jian,WANG Mei-li.L-PCA-based dimensionality reduction algorithm for high dimension images[J].Journal of Xi'an University of Science and Technology,2017,(06):906-911.[doi:10.13800/j.cnki.xakjdxxb.2017.0621 ]
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L-PCA算法下的高维图像降维算法研究(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2017年06期
页码:
906-911
栏目:
出版日期:
2017-11-30

文章信息/Info

Title:
L-PCA-based dimensionality reduction algorithm for high dimension images
文章编号:
1672-9315(2017)06-0906-06
作者:
李龙龙12何东健2王美丽3
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
Author(s):
LI Long-long12HE Dong-jian2WANG 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)
关键词:
降维算法 图像处理 主成分析
Keywords:
dimensionality reduction algorithm image processing PCA
分类号:
TP 182
DOI:
10.13800/j.cnki.xakjdxxb.2017.0621
文献标志码:
A
摘要:
文中借鉴经典凸技术聚类算法中的全局线性降维算法PCA与LDA聚类算法思想,提出了一种改进型的PCA降维算法L-PCA,该算法在保证原有样本协方差结构不变的前提下,获取变换矩阵中最重要的主分量进行赋权,通过调节类内与类间离散矩阵,使得类内距离最小化、类间聚类最大化,来搜索一个合适的映射子空间来实现不同类别数据之间的划分。通过典型数据集下的实验结果很好的验证了L-PCA算法在一阶最近近邻分类器泛化误差、准确性以及目标数据表达连续性等方面的良好性能。
Abstract:
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.

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相似文献/References:

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 LIU Ming-qin,ZHANG Xiao-guang.An efficient local fractal box-counting approach to compute fractal dimension of image[J].Journal of Xi'an University of Science and Technology,2009,(06):369.
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备注/Memo

备注/Memo:
收稿日期:2017-04-10 责任编辑:高 佳
基金项目:国家863项目(2013AA10230402); 国家自然科学基金(61402374)
通讯作者:李龙龙(1983-),男,陕西渭南人,博士,副教授,E-mail:7051110@163.com
更新日期/Last Update: 2017-12-11