NONNEGATIVE MATRIX FACTORIZATION VIA NEWTON ITERATION FOR SHARED-MEMORY SYSTEMS

被引：0
|
作者：
Flatz, Markus [1 ]
Vajtersic, Marian [1 ,2 ]
机构：
[1] Salzburg Univ, Dept Comp Sci, Salzburg, Austria
[2] Slovak Acad Sci, Dept Informat, Math Inst, Bratislava, Slovakia
来源：
PROCEEDINGS OF THE CONFERENCE ALGORITMY 2016 | 2016年
关键词：
Nonnegative Matrix Factorization; Parallel Computing; Newton Iteration;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Nonnegative Matrix Factorization (NMF) can be used to approximate a large nonnegative matrix as a product of two smaller nonnegative matrices. This paper shows in detail how an NMF algorithm based on Newton iteration can be derived utilizing the general Karush-KuhnTucker (KKT) conditions for first-order optimality. This algorithm is suited for parallel execution on shared-memory systems. It was implemented and tested, delivering satisfactory speedup results.
引用
收藏
页码：312 / 322
页数：11
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