报告题目：A New and Fast Algorithm for Symmetric Nonnegative Matrix Factorization

报告人： 新加坡国立大学储德林教授

时间：2024年12月6日16:00-17:00

腾讯会议号：928430702

储德林： 新加坡国立大学教授，德国的“洪堡学者”和日本的“JSPS学者”,多种知名国际计算数学期刊主编或副主编。近年来在SIAM Journal on Scientific Computing\SIAM Journal on Matrix Analysis and Applications\Journal of Scientific Computing \IEEE Transactions on Pattern Analysis and Machine Intelligence等国际知名学术期刊发表论文百余篇。

In this talk , the symmetric nonnegative matrix factorization ( SNMF ) is discussed . A new and fast algorithm for SNMF is introduced , which is parameter - free and updates the ables column by column . Moreover , every column is updated by solving a rank - one SNMF subproblem . The convergence to the Karush - Kuhn - Tucker ( KKT ) point set ( or the stationary point set ) is proved the new algorithm . Several synthetical and real data sets are tested to demonstrate the effectiveness and efficiency of the new algorithm . The new algorithm 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 .