[1]徐慧敏,陈秀宏.图正则化稀疏判别非负矩阵分解[J].智能系统学报,2019,14(06):1217-1224.[doi:10.11992/tis.201811021]
　XU Huimin,CHEN Xiuhong.Graph-regularized, sparse discriminant, non-negative matrix factorization[J].CAAI Transactions on Intelligent Systems,2019,14(06):1217-1224.[doi:10.11992/tis.201811021]
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图正则化稀疏判别非负矩阵分解(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1217-1224
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
Graph-regularized, sparse discriminant, non-negative matrix factorization
作者:
徐慧敏;  陈秀宏
江南大学 数字媒体学院, 江苏 无锡 214000
Author(s):
XU Huimin;  CHEN Xiuhong
School of Digital Media,Jiangnan University,Wuxi 214000,China
关键词:
非负矩阵分解; 特征提取; 降维; 流形学习; 最大间距准则; 判别信息; 稀疏约束; 线性表示
Keywords:
non-negative matrix factorization; feature extraction; dimensionality reduction; manifold learning; maximum margin criterion; discriminant information; sparse constraints; linear representation
分类号:
TP391.4
DOI:
10.11992/tis.201811021
摘要:
非负矩阵分解是一种流行的数据表示方法，利用图正则化约束能有效地揭示数据之间的局部流形结构。为了更好地提取图像特征，给出了一种基于图正则化的稀疏判别非负矩阵分解算法（graph regularization sparse discriminant non-negative matrix factorization，GSDNMF-L2，1）。利用同类样本之间的稀疏线性表示来构建对应的图及权矩阵；以L2，1范数进行稀疏性约束；以最大间距准则为优化目标函数，利用数据集的标签信息来保持数据样本之间的流形结构和特征的判别性，并给出了算法的迭代更新规则。在若干图像数据集上的实验表明，GSDNMF-L2，1在特征提取方面的分类精度优于各对比算法。
Abstract:
Non-negative matrix factorization is a popular data representation method. Using graph regularization constraints can effectively reveal the local manifold structure between data. In order to better extract image features, a graph-regularized, sparse-discriminant, non-negative matrix factorization algorithm is proposed in this paper. The sparse linear representation between similar samples was used to construct the corresponding graph and weight matrix. The objective function using the maximum margin criterion with L2,1 -norm constraint was optimized, using the tag information of the dataset to maintain the manifold structure of samples and discrimination of characteristics, and the iterative update rules of the algorithm are given. Experiments were carried out on the ORL, AR, and COIL20 datasets. Compared with other algorithms, GSDNMF-L2,1 showed higher classification accuracy in feature extraction.

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备注/Memo

备注/Memo:
收稿日期:2018-11-26。
基金项目:2018年江苏省研究生科研创新计划项目（KYCX18_1871）.
作者简介:徐慧敏,女,1994年生,硕士研究生,主要研究方向为数字图像处理、模式识别;陈秀宏,男,1964年生,教授,博士后,主要研究方向为数字图像处理和模式识别、优化理论与方法等。发表学术论文120余篇。
通讯作者:徐慧敏.E-mail:1215625771@qq.com
更新日期/Last Update: 2019-12-25