Title: 

High-dimensional Clustering via A Latent Transformation Mixture Model 

Abstract: 

Cluster analysis is a fundamental task in machine learning. Several clustering algorithms have been extended to handle high-dimensional data by incorporating a sparsity constraint in the estimation of a mixture of Gaussian models. Though it makes some neat theoretical analysis possible, this type of approach is arguably restrictive for many applications. In this work we propose a novel latent transformation mixture model for clustering. The use of unspecified transformation makes the model much more flexible than the classical model-based clustering. Under the assumption that the optimal clustering admits a sparsity structure, we develop a new clustering algorithm named CESME for high-dimensional clustering. We offer a comprehensive analysis of CESME including identifiability, initialization, algorithmic convergence, and statistical guarantees on clustering. Extensive numerical study and real data analysis show that CESME outperforms the existing high-dimensional clustering algorithms in the literature. 

Bio:

Dr. Hui Zou is currently the Dr. Lynn Y.S. Lin Professor at the University of Minnesota. He earned his Ph.D. in Statistics from Stanford University in 2005. His primary research interests include statistical learning, high-dimensional models, statistical computing, and the application of modern statistical methods in business, health, and engineering. Dr. Zou is an elected fellow of the American Association for the Advancement of Science (AAAS), the Institute of Mathematical Statistics (IMS), and the American Statistical Association (ASA). He has published over 100 research articles, many of which are highly cited, including three that have been listed among the most-cited papers of all time in JRSSB, JASA, and JCGS. One of his papers in the Annals of Statistics was selected as the Best Paper in Applied Mathematics at the 8th International Congress of Chinese Mathematicians (ICCM 2019). Dr. Zou has also mentored 15 PhD students, two of whom received the COPSS Leadership Academy Award for Emerging Leaders in Statistics.