硕士生导师
个人信息Personal Information
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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Tri-Regularized Nonnegative Matrix Tri-Factorization for Co-clustering
影响因子：8.139
DOI码：10.1016/j.knosys.2021.107101
所属单位：西南交通大学
发表刊物：Knowledge-Based Systems
刊物所在地：NETHERLANDS
项目来源：the National Key R&D Program of China (No. 2017YFB1401401)
关键字：Nonnegative matrix tri-factorizationGraph regularizationEntrywise normSparsityCo-clustering
摘要：The objective of co-clustering is to simultaneously identify blocks of similarity between the sample set and feature set. Co-clustering has become a widely used technique in data mining, machine learning, and other research areas. The nonnegative matrix tri-factorization (NMTF) algorithm, which aims to decompose an objective matrix into three low-dimensional matrices, is an important tool to achieve co-clustering. However, noise is usually introduced during objective matrix factorization, and the method of square loss is very sensitive to noise, which significantly reduces the performance of the model. To solve this issue, this paper proposes a tri-regularized NMTF (TRNMTF) model for co-clustering, which combines graph regularization, Frobenius norm, and norm to simultaneously optimize the objective function. TRNMTF can execute feature selection well, enhance the sparseness of the model, adjust the eigenvalues in the low-dimensional matrix, eliminate noise in the model, and obtain cleaner data matrices to approximate the objective matrix, which significantly improves the performance of the model and its generalization ability. Furthermore, to solve the iterative optimization schemes of TRNMTF, this study converts the objective function into elemental form to infer and provide detailed iterative update rules. Experimental results on 8 data sets show that the proposed model displays superior performance.
合写作者：TianruiLi,Shi-JinnHorng,ZengYu,XiaominWang
第一作者：Ping Deng
论文类型：学术论文
通讯作者：HongjunWang
论文编号：20212110403246
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 226
期号：107101
页面范围：1-12
ISSN号：0950-7051
是否译文：否
发表时间：2021-05-13