[1]杨静宇,郑宇杰.基于QR分解的鉴别维数压缩及其在人脸识别中的应用[J].智能系统学报,2007,2(6):48-53.
YANG Jing-yu,ZHENG Yu-jie.Discriminant dimensionality reduction based on QR decomposition and its application in face recognition[J].CAAI Transactions on Intelligent Systems,2007,2(6):48-53.
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YANG Jing-yu,ZHENG Yu-jie.Discriminant dimensionality reduction based on QR decomposition and its application in face recognition[J].CAAI Transactions on Intelligent Systems,2007,2(6):48-53.
基于QR分解的鉴别维数压缩及其在人脸识别中的应用
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
2
期数:
2007年第6期
页码:
48-53
栏目:
学术论文—机器感知与模式识别
出版日期:
2007-12-25
- Title:
- Discriminant dimensionality reduction based on QR decomposition and its application in face recognition
- 文章编号:
- 1673-4785(2007)06-0048-06
- Keywords:
- discriminant dimensionality reduction; pattern recognition; QR decomposition; di rect linear discriminant analysis (DLDA)
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 维数压缩是当前模式识别研究领域中的一个重要研究方向.但是当前部分维数压缩方法缺乏有效的鉴别信息保留机制，并且在利用Fisher鉴别准则的时候经常会遇到小样本问题 .简单介绍了维数压缩中的鉴别信息保留，并且提出了一种新的直接线性鉴别分析方法—— DLDA/QR算法.该方法首先利用矩阵的QR分解算法实现目标函数的优化，〖JP3〗再在一个较小的空间内实现有效鉴别信息的提取.在ORL人脸数据库上的实验结果验证了算法的有效性.
- Abstract:
- Dimensionality reduction is an important research topic in pattern recognition. At present, partial dimensionality reduction methods lack an effective discrimin ant information preservation mechanism. And the small sample size problem often occurs when the Fisher discriminant criterion is used. In this paper, discrimina nt information preservation is briefly presented and a new direct lnear discrim inant analysis (DLDA) method, the DLDA/QR algorithm, is suggested. With this algo rithm, the objective function is optimized through QR decomposition, and then th e ef fective discriminant information is extracted from a smaller space. Experimental results from the ORL face database demonstrate the effectiveness of the propose d method.
- 参考文献/References:
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备注/Memo
收稿日期：2007-04-17
金项目：
国家自然科学基金重点资助项目（60632050）；
国家自然科学基金资助项目（60472060）.
作者简介：
杨静宇，男，1941年生，教授，博士生导师，主要研究方向为模式识别理论与应用、智能机器人、图像分析等.1982~1984在美国伊理诺斯大学（UIUC）CSL实验室进行合作研究，1993~1994在美国密苏里大学（UMKC）计算机系担任访问教授，1998年在加拿大康科迪亚大学Concordia University)模式分析与机器智能中心担任访问教授.发表论文300余篇，论（译）著7部. E-mail: yangjy@mail.njust.edu.cn.
郑宇杰，男，1977年生，博士，主要研究方向为模式识别、人工智能、机器学习等，被SCI、EI检索论文多篇. E-mail:yjzheng13@yahoo.com.cn
更新日期/Last Update:
2009-05-08