Low Rank Tensor Completion and Its Applications

Low Rank Tensor Completion and Its Applications

Explore low rank tensor completion problems in this 55-minute virtual seminar talk by Michael Ng from The University of Hong Kong. Delve into various approaches for solving these problems, including framelet, dictionary coding, deep plug-and-play prior, multiple features, and total-variation methods. Gain insights into both theoretical and numerical results that demonstrate the effectiveness of the proposed techniques. This talk, part of the Eleventh Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series, offers valuable knowledge for researchers and practitioners in the field of industrial and applied mathematics.