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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Graph Regularized Sparse Nonnegative Matrix Factorization for Clustering
影响因子:4.747
DOI码:10.1109/TCSS.2022.3154030
所属单位:西南交通大学
发表刊物:IEEE Transactions on Computational Social Systems
关键字:Sparse matrices, Linear programming, Laplace equations, Taylor series, Optimization, Machine learning, Data models
摘要:The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l₁-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.
合写作者:李天瑞,Dexian Wang, Shi-Jinn Horng, Rui Liu
第一作者:Ping Deng
论文类型:学术论文
通讯作者:Hongjun Wang
论文编号:000656123200119
学科门类:工学
一级学科:计算机科学与技术
页面范围:1 - 12
ISSN号:2329-924X
是否译文:否
发表时间:2022-05-17