报告人：温金明 教授 暨南大学
时 间：6月2日（周四）上午10：00-11：00
地 点：腾讯会议ID：796 814 927 会议密码：123456
题 目：Binary Sparse Signal Recovery with Binary Matching Pursuit
摘 要：In numerous applications from communications and signal processing, we often need to acquire a $K$-sparse binary signal from sparse noisy linear measurements.
In this talk, we first develop an algorithm called Binary Matching Pursuit (BMP) to recover the $K$-sparse binary signal. According to whether the residual vector is explicitly formed or not at each iteration, we develop two implementations of BMP which are respectively called explicit BMP and implicit BMP. We then analyze their complexities and show that, compared to the Batch-OMP, which is the fastest implementation of OMP, the improvements of the explicit and implicit BMP}algorithms are respectively $n/(2K)$ and $K$ times when some quantities are pre-computed. Finally, we provide sharp sufficient conditions of stable recovery of the support of the sparse signal using mutual coherence and restricted isometry property of the sensing matrix.
简 介：温金明，暨南大学教授、博导、国家高层次青年人才、广东省青年珠江学者；2015年6月毕业于加拿大麦吉尔大学数学与统计学院，获哲学博士学位。从2015年3月到2018年8月，温博士先后在法国科学院里昂并行计算实验室、加拿大阿尔伯塔大学、多伦多大学从事博士后研究工作。他的研究方向主要是整数信号和稀疏信号恢复的算法设计与理论分析。他以第一作者/通讯作者在Applied and Computational Harmonic Analysis、IEEE Transactions on Information Theory、 IEEE Transactions on Signal Processing等顶级期刊和会议发表近40篇学术论文。
