讲座人介绍： 

文有为，湖南师大教授、博导生导师。香港大学数学系博士，新加坡国立大学访问研究员，香港中文大学数学系博士后。长期从事图像恢复模型与数值算法研究，相关论文发表于SIAM J. Sci. Comput., Multiscale Modeling & Simulation, SIAM J. Imaging Sci., IEEE Trans. Image Process.等领域国际权威期刊。荣获2019年度湖南省自然科学奖二等奖(第一完成人)，2013年度红云园丁奖。

讲座简介：

Tensor Robust Principal Component Analysis (TRPCA) holds a crucial position in machine learning and computer vision. It aims to recover underlying low-rank structures and characterizing the sparse structures of noise. Current approaches often encounter difficulties in accurately capturing the low-rank properties of tensors and balancing the trade-off between low-rank and sparse components, especially in a mixed-noise scenario. To address these challenges, we introduce a Bayesian framework for TRPCA, which integrates a low-rank tensor nuclear norm prior and a generalized sparsity-inducing prior. By embedding the priors within the Bayesian framework, our method can automatically determine the optimal tensor nuclear norm and achieve a balance between the nuclear norm and sparse components. Furthermore, our method can be efficiently extended to the weighted tensor nuclear norm model. Experiments conducted on synthetic and real-world datasets demonstrate the effectiveness and superiority of our method compared to state-of-the-art approaches.