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信息来源: 发布日期: 2021-03-27浏览次数:

报告题目:An Alternating Rank-k Nonnegative Least Squares Approach for Nonnegative Matrix Factorization

报告摘要:Nonnegative matrix factorization (NMF) is a prominent technique for data dimensionality reduction that has been widely used for text mining, computer vision, pattern discovery, and bioinformatics. In this talk, an approach called ARkNLS (Alternating Rank-k Nonnegative Least Squares) is introduced for computing NMF. Furthermore, a new strategy that efficiently overcomes the potential singularity problem within the context of NMF computation is also presented. Extensive numerical comparisons using real and synthetic data sets demonstrate that the proposed algorithm provides state-of-the-art performance in terms of computational accuracy and cpu time.


报告地点:腾讯会议 ID550 707 009


专家简介:新加坡国立大学教授。1982年考入清华大学,获学士、硕士、博士学位。曾先后在香港大学,清华大学,德国TU ChemnitzUniversity of Bielefeld等高校工作。主要研究领域是科学计算、数据科学、数值代数及其应用,在SIAM系列杂志,Numerische MathematikMathematics of ComputationIEEE Transactions on Pattern Analysis and Machine IntelligenceAutomatica 等国际知名学术期刊发表论文一百余篇。现任SIAM Journal on Scientific ComputingSIAM Journal on Matrix Analysis and ApplicationsAutomaticaJournal of Computational and Applied Mathematics等国际权威期刊的副主编。