检索结果-南通市图书馆

discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Quaternion Kernel Fisher Discriminant Analysis for Feature-Level Multimodal Biometric Recognition

引用

Chinese Journal of Electronics 2020年第6期29卷 1085-1092页

作者： WANG Zhifang ZHEN Jiaqi ZHU Fuzhen HAN Qi Department of Electronic Engineering Heilongjiang University Department of Computer Science and Technology Harbin Institute of Technology

Quaternion kernel Fisher discriminant analysis(QKFDA) is proposed for feature level multimodal biometric *** quaternion division ring,QKFDA extracts the most discriminative information from the quaternion fusion feature sets by maximizing the betweenclass variance while minimizing the within-class variance.A complete two-phases framework of QKFDA is developed:Quaternion kernel principal component analysis(QKPCA)plus Quaternion linear discriminant analysis(QLDA).Two experiments are designed:experiment Ⅰ fuses four different features of face and plamprint,experiment Ⅱ fuses three different features of face,plamprint and *** experimental results show that QKFDA is superior to both traditional feature fusion methods(series rule and weighted sum rule)and other quaternion feature fusion methods(QPCA,QFDA,QLPP and QKPCA).

关键词： QLDA QKFDA Other topics in statistics quaternion kernel principal component analysis Image recognition biometrics (access control) Sensor fusion feature level multimodal biometric recognition principal component analysis QLPP quaternion linear discriminant analysis QKPCA face feature fusion image fusion quaternion division ring QFDA QPCA face recognition quaternion fusion feature sets Data security Computer vision and image processing techniques feature extraction plamprint feature discriminative information quaternion kernel Fisher discriminant analysis quaternion feature fusion methods

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Balanced discriminative Transfer Feature Learning for Visual Domain Adaptation

引用

ZTE Communications 2020年第4期18卷 78-83页

作者： SU Limin ZHANG Qiang LI Shuang Chi Harold LIU Beijing Institute of Technology Beijing 100081China ZTE Corporation Shenzhen 518057China

Transfer learning aims to transfer source models to a target *** the feature matching can alleviate the domain shift effectively,but this process ignores the relationship of the marginal distribution matching and the conditional distribution ***,the discriminative information of both domains is also neglected,which is important for improving the performance on the target *** this paper,we propose a novel method called Balanced discriminative Transfer Feature Learning for Visual Domain Adaptation(BDTFL).The proposed method can adaptively balance the relationship of both distribution matchings and capture the category discriminative information of both ***,balanced feature matching can achieve more accurate feature matching and adaptively adjust itself to different *** the same time,discriminative information is exploited to alleviate category confusion during feature *** with assistance of the category discriminative information captured from both domains,the source classifier can be transferred to the target domain more accurately and boost the performance of target *** experiments show the superiority of BDTFL on popular visual cross-domain benchmarks.

关键词： transfer learning domain adaptation distribution adaptation discriminative information

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：