报告题目：Quaternion Tensor Completion and Applications
报 告 人：张扬, 加拿大曼尼托巴大学数学系终身教授
报告时间：2025年5月9日（星期五）上午10：00
报告地点：7JC214
报告摘要：Tensor (matrix) completions have wide applications in fields such as computer vision and image processing. To achieve completion, most existing methods are based on singular value decomposition and nuclear norm minimization of real tensors. However, these tensor completion methods cannot simultaneously maintain the color channel correlation and evolution robustness of color video frames, and require high computational costs to process high-dimensional data. Therefore, they have some limitations in model generalization ability and computational efficiency. In this talk, through the definition of QR decomposition and the new quaternion tensor norm, a new quaternion tensor (matrix) completion method is explored, which can well balance the model generalization ability and efficiency, and the performance of this completion is significantly improved. Numerical experiments on color images and videos prove the effectiveness of our proposed method.
报告人简介：
张扬,加拿大曼尼托巴大学数学系终身教授。曾任SCI杂志Journal of System Science and Complexity编委，现任加拿大国家自然科学和工程基金委计算机学部会评专家。主要研究矩阵和张量计算、计算机代数、符号计算及其应用。已在国际自动化顶级期刊Automatica等期刊上发表SCI论文70多篇，在Springer出版社出版专著1部，研究工作一直得到加拿大国家自然科学和工程基金委员会的连续资助。
