Sparse representation classification for battlefield textual information

摘要：

　　Sparse representation based classification has recently been shown to provide excellent results in many object recognition and classification tasks.The high cost of computing,however,is a major obstacle that limits the applicability of these methods in battlefield information process scenarios where process time and computational power is restricted.In this paper,we study a fast and computationally efficient sparse representation classification scheme for battlefield textual information in which the block sparsity of sparse coefficients is exploited.A novel sparse approximation algorithm tailored for this low complexity classification method is proposed.Experiment results show that our classification algorithm that leverages the sparse structure of the textual information outperforms plain sparse representation classification procedures in both classification accuracy and computationally efficiency.

关键词： classification sparse representation block sparsity battlefield textual information

作者: Wang Kai Liu Jingzhi Xu Shun Wang kai Gan Zhichun

作者单位: Battlefield Information Processing Laboratory Chongqing Communication Institute Chongqing,China Department of command and control system National Defense Information Institute Wuhan,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 731-734

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）