报告人：吴封盛

报告地点：格物楼3109室报告厅

报告时间：2021年5月27日19:00-19:45

报告摘要：Currently, the tensor completion problem has been paid high attention in the machine learning, especially in the field of computer vision and image processing. The low-rank tensor completion methods based on the tensor singular value decomposition and the tensor nuclear norm has been proposed. However, they have limitations in computing speed, since they are SVD-based methods and need high computational cost for high dimensional tensor. In this paper, based on the tensor QR decomposition and the tensor nuclear norm, a fast low-rank tensor completion method is proposed. By reducing the dimensions of the tensor in the nuclear norm regularization term, the performance of the completion is substantially improved. Numerical experiments for color images, MRI and videos demonstrate that the effectiveness of the proposed method.

报告人简介：吴封盛，9159金沙申请大厅计算数学方向2018级博士研究生。研究方向为张量低秩逼近理论、算法及应用。正主持9159金沙申请大厅研究生科研创新项目一项，正参与国家自然科学基金项目三项。

2021年5月27日晚，9159金沙申请大厅博士生研究吴封盛在9159金沙申请大厅3109学术报告厅作了题为《A Fast Tensor Completion Method Based on Tensor QR Decomposition and Tensor Nuclear Norm Minimization》的学术报告。本次学术报告是吴封盛同学在参加了“2021张量、超图理论及应用学术会议”后的学习分享报告，会议由学院吴莹老师主持，来自学院的多位师生参加，现场气氛热烈。

吴封盛同学以张量填充、数据恢复为背景，引出一种求解张量数据填充的快速算法，并将其应用于彩色图像数据、视频数据及MRI数据的填充和恢复。

李耀堂教授在报告后作了精彩点评，并对张量及张量填充的基本概念进行了补充讲解，让同学们对张量有了更进一步了解。

本次报告加强了学院内部包括数学方向和统计方向的研究生之间的学术学习和交流，让同学们了解了更多张量数据的处理方式，拓宽了同学们的研究视野。