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Low Rank Matrix Recovery for Seismic Data Analysis and Blind Superresolution
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Home - ActivitiesReporter：
Jinchi Chen, Postdoc, School of Big Data, Fudan University
Inviter：
Xu Zhang, Postdoc
Subject：
Low Rank Matrix Recovery for Seismic Data Analysis and Blind Superresolution
Time and place：
10:00-10:40 February 27(Monday), Tencent Meeting ID: 631-826-671
Abstract：
Low rank matrix recovery is about reconstructing a low rank matrix from incomplete measurements. It arises frequently in many research areas of science and engineering, for example, machine learning, signal processing and computer vision. Low rank matrix recovery has received extensive investigations from the theoretical and algorithmic aspects during the last decade.
In this talk, we will discuss the low rank matrix completion problem for seismic data analysis and the low rank matrix sensing problem for blind superresolution of point sources. The target matrices associated with these problems are not only low rank, but also are highly structured. Convex approaches are proposed for the corresponding low rank matrix recovery problems. Theoretical guarantees will be established, showing that nearly optimal sample complexity suffices for successful recovery.
Short Bio：
陈金池，复旦大学博士后。2019年博士毕业于北京理工大学，2019年至今在复旦大学大数据学院从事博士后研究工作。研究方向为压缩感知和强化学习。多篇论文发表在在IEEE TIT、IEEE TSP、ACHA等国际知名期刊。
In this talk, we will discuss the low rank matrix completion problem for seismic data analysis and the low rank matrix sensing problem for blind superresolution of point sources. The target matrices associated with these problems are not only low rank, but also are highly structured. Convex approaches are proposed for the corresponding low rank matrix recovery problems. Theoretical guarantees will be established, showing that nearly optimal sample complexity suffices for successful recovery.
Short Bio：
陈金池，复旦大学博士后。2019年博士毕业于北京理工大学，2019年至今在复旦大学大数据学院从事博士后研究工作。研究方向为压缩感知和强化学习。多篇论文发表在在IEEE TIT、IEEE TSP、ACHA等国际知名期刊。