题目: Geometry-Aware Locality Preserving Projection and Learning
摘要: LPP (Locality Preserving Projection) is one of commonly used dimensionality reduction algorithms for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. LPP comes with a specific locality preserving criterion. In practical application tasks such as those in computer vision, except for well-structured data like vectors, matrices or tensors, there exist data which are manifold-valued. How to extend the LPP or use LPP criterion for learning from manifold-valued data is an interesting research question. In this talk I wish showcase a couple of examples of manifolds on which LPP criterion can be implemented, including the famous Grassmann manifold, SPD matrix manifold and several new manifolds from machine learning research.
专家简介：Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. Prior to joining the University of Sydney in 2016, he was Professor in Computing from 2010 to 2016 and Associate Professor from 2005 to 2010 at Charles Sturt University (CSU). He was Senior Lecturer from Jan 2005 to July 2005 and Lecturer from Nov 2001 to Jan 2005 in the School of Mathematics, Statistics and Computer Science (now the School of Science and Technology) at University of New England (UNE). Between 1999 and 2001, he worked as a Research Fellow in the Department of Electronics and Computer Science at University of Southampton, England. Until recently his major research interest has been machine learning and its application in data science, image analysis, pattern recognition, Bayesian learning & inference, and numerical optimization etc. He is the author of 260 academic research papers and two books. His recent research has involved new machine learning algorithms for big data in business. Prof Gao won two research grants in Discovery Project theme from the prestigious Australian Research Council (ARC).