Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation  

作　　者：Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng 

机构地区：[1]the School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China [2]the School of Information Engineering,Guangzhou Panyu Polytechic,Guangzhou 511483,China [3]the Faculty of Information Technology,Monash University,20 Exhibition Walk Clayton,VIC 3800,Australia [4]IEEE [5]the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems,Macao University of Science and Technology,Macao 999078,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第12期2269-2291,共23页自动化学报（英文版）

基　　金：supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006);the National Natural Science Foundation of China (61972102);the Guangzhou Science and Technology Plan Project (023A04J1729);the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。

摘　　要：Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关 键 词：Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]