报告题目：An introduction to sparse Learning
报告人：李落清教授
报告时间：10月12日（星期五）下午3:00
报告地点：尚贤楼8楼报告厅
报告主持人：杨建伟教授
欢迎广大师生踊跃参加。
摘要：The Nyquist-Shannon sampling theorem states that if the sampling rate exceeds the Nyquist rate a signal can be recovered exactly from its samples. This talk will report the theory of compressive sampling, also known as compressed sensing or CS, a novel method to capture and represent compressible signals at a rate significantly below the Nyquist rate. This method employs nonadaptive linear projections that preserve the structure of the signal; the signal is then reconstructed from these projections using an optimization process. To make this possible, CS relies on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. As applications we explore an algorithm for linear system identication from noisy measurements.
报告人简介：
李落清，理学博士。湖北大学数学与计算机科学学院教授，博士生导师。1991年至1993年获国家留学基金委资助留学德国。曾担任第4次小波分析及其应用国际学术会议程序委员会主席和International Conference on Wavelet Analysis and Pattern Recognition (2007年、2008年、2009年) 程序委员会主席。近年来，主要研究方向为逼近论与小波分析、统计学习理论、时频分析和信号处理。
2012-10-11
