Speaker：Dr. Li Wang
University of Texas at Arlington
Title: Probabilistic Dimensionality Reduction via Structure Learning
Time：05 July 2019, 10:00
Location：物机大楼661
Abstract: We propose an alternative probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework, we present a new model, which is able to learn a set of embedding points in a lowdimensional space by retaining the inherent structure from high-dimensional data. The objective function of this new model can be equivalently interpreted as two coupled learning problems, i.e., structure learning and the learning of projection matrix. Inspired by this interesting interpretation, we propose another model, which finds a set of embedding points that can directly form an explicit graph structure. We proved that the model by learning explicit graphs generalizes the reversed graph embedding method, but leads to a natural interpretation from Bayesian perspective. This can greatly facilitate data visualization and scientific discovery in downstream analysis.
Speaker Introduction：Dr. Li Wang is currently an assistant professor with Department of Mathematics, University of Texas at Arlington, Texas, USA. She worked as a research assistant professor with Department of Mathematics, Statistics, and Computer Science at University of Illinois at Chicago, Chicago, USA from 2015 to 2017. She worked as the Postdoctoral Fellow at University of Victoria, BC, Canada in 2015 and Brown University, USA, in 2014. She received her Ph.D. degree in Department of Mathematics at University of California, San Diego, USA, in 2014. She received the master degree in Computational Mathematics from Xi'an Jiaotong University, Shaanxi, China, in 2009 and the Bachelor degree in Information and Computing Science from China University of Mining and Technology, Jiangsu, China in 2006. Her research interests include data science, polynomial optimization and machine learning.
联系人：白正简教授
