陈金池博士后, 复旦大学大数据学院

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.