计算机应用研究2018，Vol.35Issue(2)：628-631,4.DOI:10.3969/j.issn.1001-3695.2018.02.066
基于L1/2范数约束增量非负矩阵分解的SAR目标识别
SAR target recognition via incremental nonnegative matrix factorization with L1/2 constraint
摘要
Abstract
Astarget sample increasing,incremental nonnegative matrix factorization (INMF) gradually updates the decomposition model,which can effectively solve the problem that the NMF algorithm increases the computational cost.However,INMF doesn't consider that decomposition matrices on NMF can improve recognition performance when it changes NMF to have ability of incremental learning.To solve the above problem,this paper proposed incremental nonnegative matrix factorization with L1/2 constraint (L1/2-INMF),and applied it in SAR target recognition.L1/2-INMF took the decomposition matrices under L1/2 real time constraint in the incremental process,which could improve recognition performance without increase of computational complexity.According to the simulation results on MSTAR data sets,the proposed L1/2-INMF can solve the problem of traditional NMF method increasing the computational cost as sample increasing,and obtain better recognition rate than INMF.关键词
增量非负矩阵分解/合成孔径雷达/目标识别/L1/2范数约束Key words
incremental nonnegative matrix factorization (INMF)/synthetic aperture radar (SAR)/target recognition/L1/2 constraint分类
信息技术与安全科学引用本文复制引用
张慧,党思航,崔宗勇..基于L1/2范数约束增量非负矩阵分解的SAR目标识别[J].计算机应用研究,2018,35(2):628-631,4.基金项目
四川省教育厅科研资助项目(16ZB0446) （16ZB0446）
