报告题目：Efficient Nonnegative Matrix and Tensor Decomposition via A Novel Algorithm Framework
报告人：郑宁 博士 同济大学数学科学学院
报告摘要：Tensor decomposition has been widely used for the dimensional reduction and extraction of the meaningful latent features of high dimensional tensor data. In many applications, the underlying data ensemble is nonnegative and consequently the nonnegative tensor decomposition is proposed to achieve additive parts-based representation and to learn more physically interpretable results. As the corresponding tensor optimization problem has computational difficulty due to nonconvex, together with sparse, smooth, graph based Tikhonov regularization, the construction and analysis of the reliable, efficient and robust algorithms are required. Under the framework of block coordinate descent method, we aim to present a new iterative algorithm which is based on the modulus type variable transformation. The theoretical analysis of the proposed method is discussed. Numerical experiments including the synthetic data and image data show the efficiency and superiority of the proposed method comparing with the state-of-the-art methods.
报告人介绍： 郑宁，同济大学数学科学学院助理教授，日本国立信息学研究所博士后。研究兴趣为数值代数、金融计算等，在SIAM J. Matrix Analysis and Applications 等国际知名期刊上发表多篇高水平的学术论文。