QQ客服 官方微博 反馈留言

	Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems
	ZHANG WanHong ; ZHOU Tong ; HUANG BoXue ; ZHANG WanHong ; ZHOU Tong ; HUANG BoXue
	2016-03-30 ; 2016-03-30
关键词	stagewise orthogonal matching pursuit sparse solution linear underdetermined equations systems biology outlier deletion TN911.7
其他题名	Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems
中文摘要	StOMP algorithm is well suited to large-scale underdetermined applications in sparse vector estimations.It can reduce computation complexity and has some attractive asymptotical statistical properties.However,the estimation speed is at the cost of accuracy violation.This paper suggests an improvement on the StOMP algorithm that is more efficient in finding a sparse solution to the large-scale underdetermined problems.Also,compared with StOMP,this modified algorithm can not only more accurately estimate parameters for the distribution of matched filter coefficients,but also improve estimation accuracy for the sparse vector itself.Theoretical success boundary is provided based on a large-system limit for approximate recovery of sparse vector by modified algorithm,which validates that the modified algorithm is more efficient than StOMP.Actual computations with simulated data show that without significant increment in computation time,the proposed algorithm can greatly improve the estimation accuracy.; StOMP algorithm is well suited to large-scale underdetermined applications in sparse vector estimations. It can reduce computation complexity and has some attractive asymptotical statistical properties.However,the estimation speed is at the cost of accuracy violation. This paper suggests an improvement on the StOMP algorithm that is more efficient in finding a sparse solution to the large-scale underdetermined problems. Also,compared with StOMP,this modified algorithm can not only more accurately estimate parameters for the distribution of matched filter coefficients,but also improve estimation accuracy for the sparse vector itself. Theoretical success boundary is provided based on a large-system limit for approximate recovery of sparse vector by modified algorithm,which validates that the modified algorithm is more efficient than StOMP. Actual computations with simulated data show that without significant increment in computation time,the proposed algorithm can greatly improve the estimation accuracy.
语种	英语 ; 英语
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/147130]
专题	清华大学
推荐引用方式 GB/T 7714	ZHANG WanHong,ZHOU Tong,HUANG BoXue,et al. Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems[J],2016, 2016.
APA	ZHANG WanHong,ZHOU Tong,HUANG BoXue,ZHANG WanHong,ZHOU Tong,&HUANG BoXue.(2016).Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems..
MLA	ZHANG WanHong,et al."Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems".(2016).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。