时间：6月6号（周二）下午15:30-16：30
地点：逸夫楼428室
报告人：储德林教授（新加坡国立大学）
Symmetric Nonnegative Matrix Factorization
Abstract
In this talk, the symmetric nonnegative matrix factorization (SNMF), which is a powerful tool in data mining for data dimension reduction and clustering, is discussed. Our present work is introduced including: (i) a new descent direction for the rank-one SNMF is derived and a strategy for choosing the step size along this descent direction is established; (ii) a progressive hierarchical alternating least squares (PHALS) method for SNMF is developed, which is parameter-free and updates the variables column by column. Moreover, every column is updated by solving a rank-one SNMF subproblem; and (iii) the convergence to the Karush-Kuhn-Tucker (KKT) point set (or the stationary point set) is proved for PHALS. Several synthetical and real data sets are tested to demonstrate the effectiveness and efficiency of the proposed method. Our PHALS provides better performance in terms of the computational accuracy, the optimality gap, and the CPU time, compared with a number of state-of-the-art SNMF methods.
邀请人：张雷洪