- Title： Subspace Clustering and Its Development for Manifold-valued Data
- Date：10:00pm US East time, 10/15/2022
- Date：10:00am Beijing time, 10/16/2022
- Zoom ID：933 1613 9423
- Zoom PWD：416262
Speaker: Professor Junbin Gao, The Professor of Big Data Analytics, The University of Sydney Business School, The University of Sydney, NSW 2006 Australia
In this talk I will review my research on the manifold-valued data analysis and its applications in machine learning tasks such as clustering and classification. Particularly I will introduce how the subspace clustering approach is applied to clustering typical manifold-valued data widely seen in computer vision such as Grassmannian manifolds, Stiefel manifold, SPD manifold, and functional data. The topics to be covered include multidimensional data analysis, machine learning algorithm for manifold-valued data, advanced deep learning for multidimensional data, and their potential applications in modern business and economics domains.
Professor Junbin Gao is the Professor of Big Data Analytics in the University of Sydney Business School. Prior to his USyd appointment, he was a Professor in Computing Science in the School of Computing and Mathematics and the deputy Director of Centre for Research in Complex Systems (CRiCS) at Charles Sturt University (CSU). He was a senior lecturer, a lecturer in Computer Science at University of New England, Australia. He was an associate lecturer, lecturer, associate professor and professor in Department of Mathematics at Huazhong University of Science and Technology, prior coming to Australia. Professor Gao obtained his PhD from Dalian University of Technology, China. His main research interests include machine learning, data mining, Bayesian learning and inference, and business analysis. His research is supported by two ARC (Australian Research Council) DP grants and two larger industrial grants. He has published more than 300 research papers in top journals (such as IEEE PAMI, NNLS, IP) and top conferences (CVPR, ICML, AAAI, IJCAI etc). In 2014, his research work on dimensionality reduction was highly featured by The Australian, a well-known newspaper.
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