Manhattan Nonnegative matrix factorization using the alternating direction method of multipliers

被引：0

作者：

Cao, Chan ^{[1
]}

Tang, Shuyu ^{[1
]}

Zhang, Nian ^{[2
]}

Dai, Xiangguang ^{[1
]}

Zhang, Wei ^{[1
]}

Feng, Yuming ^{[1
]}

Xiong, Jiang ^{[1
]}

Liu, Jinkui ^{[1
]}

Thompson, Lara ^{[3
]}

机构：

[1] Chongqing Three Gorges Univ, Chongqing 404100, Peoples R China

[2] Univ Dist Columbia, Dept Elect & Comp Engn, Washington, DC 20008 USA

[3] Univ Dist Columbia, Dept Mech Engn, Biomed Engn Program, Washington, DC 20008 USA

来源：

2023 15TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE, ICACI | 2023年

基金：

美国国家卫生研究院;

关键词：

Nonnegative matrix factorization; L-1-norm; nonconvex nonsmooth; PARTS;

D O I：

10.1109/ICACI58115.2023.10146156

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nonnegative matrix factorization (NMF) was a classic model for dimensional reduction. Manhattan NMF is a variant version of NMF that uses a L-1-norm cost function as the objective function instead of the L-2-norm cost function. Manhattan NMF can be formulated as a nonconvex nonsmooth optimization problem. An algorithm framework for solving the Manhattan NMF problem based on the alternating direction method of multiplication is presented to us. Compared with the existed algorithm, our proposed algorithm is more effective by experiments on synthetic and real data sets.

引用

页数：5

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