报告时间：10月14日上午 10:30-11:30
腾讯会议 ID：966 126 912
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.
报告人简介：郑宁博士，同济大学99905银河在线登录平台助理教授。同济大学应用数学本科，获同济大学数学系理学博士学位和日本综合研究大学院大学信息学博士学位，获上海市优秀博士毕业生和日本国立信息学研究所年度最佳研究等荣誉称号。曾在日本国立信息学研究所和日本理化学研究所担任特别研究员职务。主要从事约束不相容系统快速算法和美式期权定价数值方法研究，在SIAM，NLAA，JSC等杂志上发表多篇学术论文。
邀请人：张雷洪