[1]朱换荣,郑智超,孙怀江.面向局部线性回归分类器的判别分析方法[J].智能系统学报,2019,14(5):959-965.[doi:10.11992/tis.201808007]
ZHU Huanrong,ZHENG Zhichao,SUN Huaijiang.Locality-regularized linear regression classification-based discriminant analysis[J].CAAI Transactions on Intelligent Systems,2019,14(5):959-965.[doi:10.11992/tis.201808007]
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ZHU Huanrong,ZHENG Zhichao,SUN Huaijiang.Locality-regularized linear regression classification-based discriminant analysis[J].CAAI Transactions on Intelligent Systems,2019,14(5):959-965.[doi:10.11992/tis.201808007]
面向局部线性回归分类器的判别分析方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第5期
页码:
959-965
栏目:
学术论文—机器学习
出版日期:
2019-09-05
- Title:
- Locality-regularized linear regression classification-based discriminant analysis
- Keywords:
- locality-regularized linear regression classification; dimensionality reduction; orthogonal projections; trace ratio problem; face recognition; feature extraction; discriminant analysis; linear regression classification
- 分类号:
- TP391
- 摘要:
- 局部线性回归分类器（locality-regularized linear regression classification，LLRC）在人脸识别上表现出了高识别率以及高效性的特点，然而原始特征空间并不能保证LLRC的效率。为了提高LLRC的性能，提出了一种与LLRC相联系的新的降维方法，即面向局部线性回归分类器的判别分析方法（locality-regularized linear regression classification based discriminant analysis，LLRC-DA）。LLRC-DA根据LLRC的决策准则设计目标函数，通过最大化类间局部重构误差并最小化类内局部重构误差来寻找最优的特征子空间。此外，LLRC-DA通过对投影矩阵添加正交约束来消除冗余信息。为了有效地求解投影矩阵，利用优化变量之间的关系，提出了一种新的迹比优化算法。因此LLRC-DA非常适用于LLRC。在FERET和ORL人脸库上进行了实验，实验结果表明LLRC-DA比现有方法更具有优越性。
- Abstract:
- Locality-regularized linear regression classification (LLRC) based face recognition achieves high accuracy and high efficiency. However, the original feature space cannot guarantee the efficiency of LLRC. To improve the performance of LLRC, this study proposes a new dimensionality reduction method called locality-regularized linear regression classification-based discriminant analysis (LLRC-DA), which is directly associated with LLRC. The objective function of LLRC-DA is designed according to the classification rule of LLRC. In LLRC, interclass local reconstruction errors are maximized and simultaneously, intraclass local reconstruction errors are minimized to identify the optimal feature subspace. In addition, LLRC-DA eliminates redundant information using an orthogonal constraint, imposed on the projection matrix. To effectively obtain the solutions of the projection matrix, we exploit the relationship between optimal variables and propose a new trace ratio optimization method. Hence, LLRC-DA suits LLRC well. Extensive experimental results obtained from the FERET and ORL face databases demonstrate the superiority of the proposed method than state-of-the-art methods.
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备注/Memo
收稿日期:2018-08-09。
基金项目:国家自然科学基金项目（61772272）.
作者简介:朱换荣,女,1994年生,硕士研究生,主要研究方向为机器学习、人脸识别;郑智超,男,1992年生,博士研究生,主要研究方向为人脸识别、子空间学习;孙怀江,男,1968年生,教授,博士生导师,主要研究方向为神经网络与机器学习、人体运动分析与合成、多媒体与虚拟现实、图像处理与计算机视觉。曾主持或参与完成国家级项目3项,省部级项目3项,获省部级科技进步二等奖1项。发表学术论文80余篇。
通讯作者:朱换荣.E-mail:zhuhuanrong@foxmail.com
更新日期/Last Update:
1900-01-01