数学与统计学院学术报告[2021] 050号

(高水平大学建设系列报告550号)

报告题目: Graph regularized low-rank representation for submodule clustering

报告人： 吴桐（新泽西州立大学）

报告时间：2021年6月10日下午14:30-16:30

腾讯会议id：240 905 327

报告内容：In this talk, I will present a new submodule clustering method for imaging (2-D) data. Unlike most existing clustering methods that first convert such data into vectors as preprocessing, the proposed method arranges the data samples as lateral slices of a third-order tensor. Our algorithm is based on the union-of-free-submodules model and the samples are represented using t-product in the third-order tensor space. First, a low-rank constraint on the representation tensor is imposed to capture the principle information of data. By incorporating manifold regularization into the tensor factorization, the proposed method explicitly exploits the local manifold structure of data. Meanwhile, a segmentation dependent term is employed to integrate the two pipeline steps of affinity learning and spectral clustering into a unified optimization framework. Finally, a nonlinear extension is proposed to handle data drawn from a mixture of nonlinear manifolds.

报告人简历：吴桐，2009年获得上海交通大学工学学士学位，2012年和2017年分别在美国杜克大学和罗格斯大学获得硕士和博士学位。研究兴趣包括机器学习，信号与图像处理，以及计算机视觉。

欢迎有兴趣的老师和同学参加！

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                                              2021年6月7日