Approximation from noisy data
数学专题报告
报告题目(Title)：Approximation from noisy data
报告人(Speaker)：杨建斌 （河海大学）
地点(Place)：腾讯会议：419-255-068
时间(Time)：2021/12/10 15:00-16:00
邀请人(Inviter)：蔡永强
报告摘要
Approximation of functions from observed data is often needed. This has been widely studied in the literature when data is exact and the underlying function is smooth. However, the observed data is often contaminated with noise and the underlying function may be nonsmooth. To properly handle noisy data, any effective approximation scheme must contain a noise removal component. To well approximate nonsmooth functions, one needs to have a sparse approximation in, for example, the wavelet domain. This talk presents theoretical analysis of such noise removal schemes through the lens of function approximation. For a given sample size, approximation from uniform grid data and scattered data is investigated.
主讲人简介
杨建斌，河海大学理学院副教授。2010年于浙江大学取得应用数学博士学位，曾在新加坡国立大学、加拿大阿尔伯塔大学等机构从事访问学者工作。研究兴趣包括小波分析及应用、图像和曲面处理、计算生物等。主持国家基金青年项目、面上项目等科研项目。在SIAM J. Numer. Anal., J. Comput. Phys., J. Approx. Theory 等期刊发表学术论文二十余篇。
