数苑博雅讲座（62）：Tensor Representations in Data Science
来源：
发布时间：2026-05-29
报告时间：- 2026年3月23日 （周一）上午10:00-11:00
- 报告地点：
- 国交2栋315会议室
- 报告人：
- 吴国宝
- 报告人单位：
- 香港浸会大学
- 邀请人：
- 张雄军
- 摘要：
- Higher-order tensors are suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. In this talk, we discuss the recent development of tensor representations in data science. Both theoretical results and numerical examples are presented to demonstrate the usefulness of tensor representations.
