报告时间：5月18日周三上午10：00-11：00
线上报告：腾讯会议 454-114-410

主办单位： 数学与信息学院

摘要：Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order-d (d ≥ 4) tensors are commonly encountered in real-world applications, like fourth-order color videos, fifth-order light-field images, and sixth-order bidirectional texture functions. Aiming at addressing this critical issue, this talk reported an order-d tensor recovery framework including the model, algorithm and theories by innovatively developing a novel algebraic foundation for order-d t-SVD, thereby achieving exact completion for any order-d low t-SVD rank tensors with missing values with an overwhelming probability. Emperical studies on synthetic data and real-world visual data illustrate that compared with other state-of-the-art recovery methods, the proposed one achieves highly competitive performance in terms of both qualitative and quantitative metrics.

王建军，博士，西南大学三级教授，博士生导师，重庆市学术带头人，重庆市创新创业领军人才，巴渝学者特聘教授，重庆工业与应用数学学会副理事长，CSIAM全国大数据与人工智能专家委员会委员，美国数学评论评论员，曾获重庆市自然科学奖励。主要研究方向为：高维数据建模、机器学习（深度学习）、数据挖掘、压缩感知、张量分析、函数逼近论等。在神经网络(深度学习)逼近复杂性和高维数据稀疏建模等方面有一定的学术积累。主持国家自然科学基金5项，教育部科学技术重点项目1项，重庆市自然科学基金1项，主研8项国家自然、社会科学基金，参与国家重点基础研究发展‘973’计划一项；现主持国家自然科学基金面上项目1项， 多次出席国际、国内重要学术会议，并应邀做大会特邀报告30余次。 已在IEEE Transactions on Pattern Analysis and Machine Intelligence（2）, IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning System（2），Applied and Computational Harmonic Analysis(2),Inverse Problems, Neural Networks, Signal Processing(2), IEEE Signal Processing letters(2), Journal of Computational and applied mathematics, ICASSP，IET Image processing(2), IET Signal processing(4),中国科学(A,F辑)(4), 数学学报, 计算机学报, 电子学报(3)等知名专业期刊发表90余篇学术论文,IEEE等系列刊物，National Science Review 及Signal Processing，Neural Networks，Pattern  Recognization，中国科学, 计算机学报，电子学报，数学学报等知名期刊审稿人。

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