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学术报告

【北航数学论坛(6月26日,佘轶原)】Rank-constrained Inherent Clustering for Multivariate Supervised Learning
发布日期:2019-06-28  浏览量:

北航数学论坛

题目: Rank-constrained Inherent Clustering for Multivariate Supervised Learning

报告人:佘轶原 教授(佛罗里达州立大学)

时间:626日(星期三)1600-1700

地点:主321

摘要:Modern clustering applications are often faced with challenges from high dimensionality, nonconvexity and parameter tuning. This paper gives a mathematical formulation of low-rank clustering and proposes an optimization based inherent clustering framework. The  method  enjoys a nice kernel property to apply to similarity data and can be extended to supervised learning. By use of linearization and block coordinate descent, a simple-to-implement   algorithm is developed, which  performs subspace learning and clustering iteratively. Our non-asymptotic analyses show a tight error rate of   rank constrained inherent clustering and its minimax optimality, along with   a new information criterion  for parameter tuning in jointly rank-deficient and equi-sparse models. These results are the first of their kind in multivariate supervised learning and show interesting differences  from those obtained for supervised learning with sparsity.   Extensive simulations and real-data experiments demonstrate the excellent performance of the proposed approach.

报告人简介:佘轶原,佛罗里达州立大学教授。2008年获得斯坦福大学统计学博士学位,2008起任职于佛罗里达州立大学统计系,曾获得  NSF CAREER AwardFlorida State University Developing Scholar Award 等奖项。教授担任  Metrika IEEE Transactions on Network Science and Engineering 以及Journal of the American Statistical Association等顶级杂志的编委。他的主要研究方向包括:高维统计、统计机器学习、优化、信号处理、稳健统计和网络科学领域。

邀请人:陈迪荣 

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