报告题目：Multiscale Data Analysis: Framelets, Manifolds and Graphs
主讲： Dr Xiaosheng Zhuang
日期：2017 年5 月22日 (周一)
时间：上午10:00 – 11:00
While Big Data are high-volume, high-dimensional, and high complexity, 、they are typically concentrated on low-dimensional manifolds or can be represented by graphs, digraphs, etc. Sparsity is the key to the successful analysis of data in various forms. Multiscale representation systems provide efficient and sparse representation of various data sets. In this talk, we will discuss the characterizations, construction, and applications of framelets on manifolds and graphs. We shall demonstrate that tight framelets can be constructed on compact Riemannian manifolds or graphs, and fast algorithmic realizations exist for framelet transforms on manifolds and graphs. Explicit construction of tight framelets on the sphere as well as numerical examples will be shown.
Dr Xiaosheng Zhuang received his bachelor's degree and master's degree in mathematics from Sun Yat-Sen University, China, in 2003 and 2005, respectively. He received his PhD in applied mathematics from University of Alberta, Canada, in 2010. He was a Postdoctoral Fellow at Universität Osnabrück in 2011 and Technische Universität Berlin in 2012. His research interest includes directional multiscale representation systems, image/signal processing, and compressed sensing.