Event Details

Dr Mini Zhang, University College Dublin will present a seminar to the Management Science Department

Abstract: We develop an ensemble clustering framework for identifying the latent cluster labels of functional data that are generated from a Gaussian process mixture. Our approach capitalizes on a critical feature of Gaussian random functions: When the functional data are from a Gaussian process mixture, the projection coefficients of the functional data onto any given projection function conform to a univariate Gaussian mixture model (GMM). Therefore, through the execution of multiple one-dimensional projections and the learning of a univariate GMM for each projection, we will obtain an ensemble of GMMs. With each GMM representing a base clustering, we then apply the ensemble clustering technique to derive the consensus clustering. The computational complexity for identifying the cluster labels is much lower than that of state-of-the-art methods, and we provide theoretical guarantees on the identifiability and learnability of Gaussian process mixtures. Extensive experimentation on both synthetic and real datasets validates the superiority of our method over existing techniques.

Bio: Mimi Zhang joined TCD as an assistant professor in October 2017. She holds a B.Sc. in statistics from University of Science and Technology of China (Sep. 2007-Jul. 2011), and a Ph.D. in industrial engineering from City University of Hong Kong (Nov. 2011-Jan. 2015). Before joining TCD, she was a research associate at University of Strathclyde and Imperial College London. Her main research areas are machine learning and operations research, including cluster analysis, Bayesian optimization, functional data analysis, reliability & maintenance (engineering), etc. She is the strand leader of the Data Science MSc programme and an AE for Journal of Classification.