组稀疏模型及其算法综述  

Survey on Group Sparse Models and Algorithms

作　　者：刘建伟[1] 崔立鹏[1] 罗雄麟[1] 

机构地区：[1]中国石油大学(北京)自动化研究所,北京102249

出　　处：《电子学报》2015年第4期776-782,共7页Acta Electronica Sinica

基　　金：国家自然科学基金(No.21006127)

摘　　要：稀疏性与组稀疏性在统计学、信号处理和机器学习等领域中具有重要的应用.本文总结和分析了不同组稀疏模型之间的区别与联系,比较了不同组稀疏模型的变量选择能力、变量组选择能力、变量选择一致性和变量组选择一致性,总结了组稀疏模型的各类求解算法并指出了各算法的优点和不足.最后,本文对组稀疏模型未来的研究方向进行了探讨.The sparsity and group sparsity have important applications in the statistics, signal processing and machine learning. This paper summarized and analyzed the differences and relations between various group sparse models. In addition, we compared different models＇ variable selection ability, variable group selection ability, variable selection consistency and variable group selection consistency. We also summarized the algorithms of group sparse models and pointed the advantages and disadvantages of the algorithms. Finally, we point out the future research directions of the group sparse models.

关 键 词：稀疏性 组稀疏性 变量选择 变量组选择 一致性 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]