学术海报

12月12日 刘一鸣博士学术报告(数学与统计学院)

发布者:胡永斌发布时间:2019-12-11浏览次数:596

报 告 人: 刘一鸣 博士

报告题目:High dimensional clustering Covariance clustering for mixture data

报告时间:2019年12月12日(周四)上午10:00

报告地点:静远楼1508学术报告厅

主办单位:数学与统计学院、科学技术研究院

报告摘要:

       Clustering is an essential subject in unsupervised learning. It is a common technique used in many fields, including machine learning, statistics, bioinformatics, and computer graphics. To classify different samples into a homogeneous group, it is based on different criterions. In this paper, we focus on the clusters that are characterized by the different covariances, and we study the clustering method for the high dimensional mixtures. According to this setting, we propose a new approach,covariance clustering  method, to conduct clustering.  Both theoretical and numerical properties of the covariance clustering method are discussed.Specifically, we propose  one algorithm that is applicable to do the clustering in different settings. In addition, we prove that the misclustering error for this algorithm converges to zero with probability tends to one under mild conditions. Simulation studies also demonstrate that the covariance clustering method outperforms other methods under a variety of settings.