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

作　　者：Qi ZHANG Rui LI Tianguang CHU 

机构地区：[1]School of Information Technology&Management,University of International Business&Economics,Beijing 100029,China [2]School of Mathematical Sciences,Dalian University of Technology,Dalian 116024,China [3]State Key Laboratory for Turbulence and Complex Systems,College of Engineering,Peking University,Beijing 100871,China [4]Key Laboratory of Machine Perception(Ministry of Education),Peking University,Beijing 100871,China

出　　处：《Science China(Information Sciences)》2020年第1期243-245,共3页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61673027, 61503375);Fundamental Research Funds for the Central Universities (Grant Nos. CXTD10-05, 18QD18 in UIBE, DUT19LK18)

摘　　要：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 class exhibits separate clustering)and mixmodal(i.e.,data from different classes has mixed modality)data[1],we have presented a semi-supervised graph embedding(SGE)model in[2]to incorporate the soft label information with hierarchical locality of data.Through the maximizing process upon the weighted between-class separability as well as the minimizing processes upon the localitypreserved within-class and scaled overall-class data distances respectively,the intrinsic characters of data with multimodal or mixmodal distributing properties can be well captured.

关 键 词：MODAL EMBEDDING EXTRACTING 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]