報告題目：Inherent Supervised Clustering with Low Rank 2
報 告 人：佘轶原 教授 佛羅裡達州立大學
報告時間：2020年6月23日 9:00-10:00
報告地點：騰訊會議ID：483 592 372
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校内聯系人：程建華 chengjh@jlu.edu.cn
報告摘要：
Modern high-dimensional methods commonly adopt the "bet on sparsity" principle while in the big-data era statisticians often face "dense" problems with large numbers of unknowns. This paper gives a mathematical formulation of low-rank supervised clustering to automatically group the predictors in building a multivariate predictive model. By use of linearization and block coordinate descent, a simple-to-implement algorithm is developed, which performs subspace learning and clustering iteratively with guaranteed convergence. We show a tight error bound of the proposed method, study its minimax optimality, and propose a new information criterion for parameter tuning, all with distinctive rates from the large body of literature based on sparsity. Extensive simulations and real-data experiments demonstrate the excellent performance of rank-constrained inherent clustering.
報告人簡介：
佘轶原，佛羅裡達州立大學統計系教授，2008年畢業于斯坦福大學，獲得統計學博士學位。佘教授的主要研究方向包括：高維統計、統計機器學習、優化、信号處理、穩健統計和網絡科學等領域，曾獲得NSF CAREER Award，Florida State University Developing Scholar Award 等獎項，并先後擔任Metrika，IEEE Transactions on Network Science and Engineering以及Journal of the American Statistical Association等頂級雜志的編委。
