硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Biased unconstrained non-negative matrix factorization for clustering
影响因子：8.139
DOI码：10.1016/j.knosys.2021.108040
所属单位：西南交通大学
发表刊物：KNOWLEDGE-BASED SYSTEMS
刊物所在地：NETHERLANDS
关键字：Non-negative matrix factorization;Unconstrained regularization;Stochastic gradient descent;Clustering
摘要：Clustering remains a challenging research hotspot in data mining. Non-negative matrix factorization (NMF) is an effective technique for clustering, which aims to find the product of two non-negative low-dimensional matrices that approximates the original matrix. Since the matrices must satisfy the non-negative constraints, the Karush–Kuhn–Tucker conditions need to be used to obtain the update rules for the matrices, which limits the choice of update methods. Moreover, this method has no learning rate and the updating process is completely dependent on the data itself. In addition, the two low-dimensional matrices in NMF are randomly initialized, and the clustering performance of the model is reduced. To address these problems, this paper proposes a biased unconstrained non-negative matrix factorization (BUNMF) model, which integrates the norm and adds bias. Specifically, BUNMF uses a non-linear activation function to make elements of the matrices to remain non-negative, and converts the constrained problem into an unconstrained problem. The matrices are renewed by sequentially updating the matrices’ elements using stochastic gradient descent to obtain an update rule with a learning rate. Furthermore, the BUNMF model is constructed by three different activation functions and their iteration update algorithms are given through detailed reasoning. Finally, experimental results on eight public datasets show the effectiveness of the proposed model.
合写作者：Fan Zhang,李天瑞,Shi-Jinn Horng
第一作者：Ping Deng
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20220311460171
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 239
期号：5 March 2022
页面范围：108040
ISSN号：0950-7051
是否译文：否
发表时间：2021-12-29