题  目：Principal Graph and Structure Learning based on Reversed Graph Embedding

主讲人：Wang Li美国德州大学阿灵顿分校(University of Texas at Arlington)助理教授

时  间：2018年1月4日 下午3:00-4:00

地  点:同济大厦A楼208室

报告内容摘要

Abstract:

Many scientific datasets are of high dimension, and the analysis usually requires retaining the most important structures of data. Many existing methods work only for data with structures that are mathematically formulated by curves, which is quite restrictive for real applications.To get more general graph structures, we develop a novel principal graph and structure learning framework that captures the local information of the underlying graph structure based on reversed graph embedding.A new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously.Experimental results on various synthetic and real world datasets show that the proposed can uncover the underlying structure correctly.

报告人简介

王莉(Wang Li), 美国德州大学阿灵顿分校(University of Texas at Arlington)助理教授，博士毕业于美国加州大学圣地亚哥分校(University of California, San Diego)，主要从事优化理论和方法，大数据，机器学习等方面的研究。已经在SIAM Journalon Optimization， Nature Methods等期刊发表20多篇论文。