学术活动

当前位置: 首页>>通知公告>>学术活动>>正文
 

信息学院学术报告(2019年第11讲:)Sparse Bayesian Learning Using Approximate Message Passing with Unitary Transformation

2019年07月22日 11:02  点击:[]


报告人: 郭庆华教授

报告时间:20197月22日上午9

报告地点:包玉书7号楼210会议室

摘要:The conventional sparse Bayesian learning (SBL) algorithm suffers from high computational complexity. Recently, SBL has been implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it is vulnerable to ‘difficult’ measurement matrices as AMP can easily diverge. Damped AMP has been used to alleviate the problem at the cost of significantly slowing the convergence rate. In this talk, I will introduce a new low complexity SBL algorithm, which is designed based on the AMP with unitary transformation (UTAMP). I will show that, compared to state-of-the-art AMP based SBL algorithms, our proposed UTAMP-SBL is much more robust and converges much faster, leading to remarkably better performance. In many cases, the performance of the algorithm can approach the support-Oracle MMSE bound closely.

 
Copyright © 2010-2011 宁波大学信息科学与工程学院 POWERED BY EECS!
学院地址:浙江省宁波市江北区风华路宁波大学北校区
电话:0574-87609488,传真:0574-87600940