内容簡介：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.

報告人：Michael Ng (吳國寶)  教授

報告人簡介：Michael Ng (AMS Fellow, SIAM Fellow, AAIA Fellow) is the Dean of Science, Dr. Elizabeth K.S. Law Endowed Professor in Data Science, Chair Professor in Mathematics, Chair Professor in Data Science, and Chair Professor (Affiliate) in Department of Computer Science, Hong Kong Baptist University. His research interests include bioinformatics, image processing, scientific computing, and data mining. He is selected for the 2025 Class of Fellows of the American Mathematical Society and the 2017 Class of Fellows of the Society for Industrial and Applied Mathematics. He obtained the Feng Kang Prize for his significant contributions in scientific computing. He serves on the Editorial Board members of several international journals. (https://sites.google.com/view/michael-ng-math/home)

題目二：The tail-atomic norm methodology and the profile analyses of the tail-l2 minimization

内容簡介：An effective tail-atomic norm methodology for gridless spectral estimations are developed with a tail minimization mechanism. We prove that the tail-atomic norm is equivalent to a positive semi-definite programming (PSD) problem. The tail-atomic norm algorithm is more robust to noise than other related methodologies. Moreover, we conduct profile analyses on the tail-l2 minimization and establish an equivalent two-stage formulation. A novel error bound of tail-l2 minimization problem is derived and the tail-l2 profile algorithm shows superior performance on sparse recovery and robustness against noise.

報告人：冼軍  教授

報告人簡介：博士生導師, 現為中山大學數學學院教授，中國數學會理事、廣東省數學會理事、廣東省工業與應用數學學會副理事長、廣東省計算數學重點實驗室副主任。2004年畢業于中山大學基礎數學專業，獲理學博士學位, 同年進入浙江大學數學博士後流動站, 2006年博士後出站至今在中山大學數學學院工作。主要研究方向為小波分析與應用調和分析、采樣理論及其在信号處理中的應用。在Appl. Comput. Harmon. Anal., Inverse Probl., J. Fourier Anal. Appl., Proc. Amer. Math. Soc., J. Approx. Theory等國内外主流專業期刊發表多篇關于信号的采樣與重構理論及其應用的論文, 部分結果獲得同行們的關注；曾作為項目負責人主持包括國家優秀青年基金在内的多項國家級和省部級基金項目；以第一負責人獲2023年度廣東省自然科學二等獎。

時  間：2024年11月9日（周六）上午9：30開始

地  點：南海樓124室

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太阳集团1088vip

2024年11月5日