A recursive least squares algorithm with l_(1) regularization for sparse representation  

作　　者：Di LIU Simone BALDI Quan LIU Wenwu YU 

机构地区：[1]School of Cyber Science and Engineering,Southeast University,Nanjing 210096,China [2]School of Computation,Information and Technology,Technical University of Munich,Garching 85748,Germany [3]School of Mathematics,Center for Mobile Communication and Security,Southeast University,Nanjing 210096,China [4]School of Artificial Intelligence,Southeast University,Suzhou 215100,China

出　　处：《Science China(Information Sciences)》2023年第2期277-278,共2页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No. 62073074);Key Intergovernmental Special Fund of National Key Research and Development Program (Grant No. 2021YFE0198700);Research Fund for International Scientists (Grant No. 62150610499)。

摘　　要：Sparse representation aims to identify a few basic elements in a signal, so as to use a combination of such elements to reconstruct the original signal. The l_(1)-norm has been widely applied in sparse representation, either when processing batches of data offline, or online as in adaptive filtering [1–3].

关 键 词：REPRESENTATION SIGNAL SPARSE 

分 类 号：TN911[电子电信—通信与信息系统]