Kernel semi-supervised graph embedding model for multimodal and mixmodal data

被引：0
作者：
Qi ZHANG [1 ,2 ]
Rui LI [3 ]
Tianguang CHU [4 ]
机构：
[1] School of Information Technology & Management, University of International Business & Economics
[2] Key Laboratory of Machine Perception(Ministry of Education), Peking University
[3] School of Mathematical Sciences, Dalian University of Technology
[4] State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University
来源：
ScienceChina(InformationSciences) | 2020年 / 63卷 / 01期
关键词：
Kernel semi-supervised graph embedding model for multimodal and mixmodal data;
D O I：
暂无
中图分类号：
TP181 [自动推理、机器学习]; TP391.41 [];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ; 080203 ;
摘要：
<正>Dear editor,Semi-supervised learning has obtained increasing interests in machine learning, because making use of both labeled and unlabeled training samples helps extracting discriminative features and meanwhile reduces the time-consuming and labor-intensive labeling burden. For extracting features upon multimodal (i.e., data of the same
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页码：247 / 249
页数：3
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