寧波大學

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信息學院學術報告(2019年第11講:)Sparse Bayesian Learning Using Approximate Message Passing with Unitary Transformation

發布日期:2019-07-22 作者:信息科學與工程學院 文章來源:  責任編輯: 瀏覽量:

報告人: 郭慶華教授

報告時間:2019年7月22日上午9點

報告地點:包玉書7號樓210會議室

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.


上一條:新藥院學術報告 總44講(2019年第23講)---從有機分子反應體系到金屬有機框架體系中化學反應及分子相互作用的計算探究 下一條:機械學院學術報告——自適應光學波前改正器及相關研究

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