报告人：Jianhong Wu
报告地点：腾讯会议ID:513 287 233
报告时间：2022年10月19日星期三8:30-9:30
邀请人：范猛
报告摘要：
I will try to present a novel dynamical system approach (inverse process of pattern formulation) towards data-driven high-dimensional data clustering. In this approach, we construct a feedforward and feedback neural network that self-organizes high-dimensional data into clusters characterized by the network’s local attractors and their domains of attraction whose boundaries describe the clustering criteria. For a high-dimensional data, clusters (and local attractors) are formed in lower-dimensional subspaces not pre-prescribed, and delay adaptation governed by the dissimilarity between features of emerging clusters and input signals is the critical feedback mechanism for the convergence of the subspaces where clusters are formed. These subspaces can be skewed, so the principal component analysis must be incorporated into the delay adaptation. We will illustrate the global convergence theory and algorithm with some real-life applications.
主讲人简介：
Professor Jianhong Wu, a fellow of the Royal Society of Canada, fellow of Canadian Academy of Health Sciences, University Distinguished Research Professor of York University, Canada Research Chair of Applied Mathematics, and Chang Jiang scholar visiting professor, is the co-editor-in-chief of Infectious Disease Modeling and Big Data Information Analysis, and a member of the editorial boards of journals such as Journal of Mathematical Biology and IEEE Transactions on the Pattern Analysis and Machine Intelligence. He has published 8 monographs and more than 450 academic papers in Kluwer, AMS/Fields, Springer, Wiley, and other publishers. He has won more than 10 academic achievement awards, including the Queen's Diamond Jubilee medal of Canada, a lifetime Fields Institute Fellow, the Outstanding Achievement Award for Canadian Chinese Professionals, and the Canadian New Pioneer Technology Award.
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