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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Dual Graph-regularized Sparse Concept Factorization for Clustering
影响因子：7.5
DOI码：10.1016/j.ins.2022.05.101
所属单位：西南交通大学
发表刊物：Information Sciences
刊物所在地：UNITED STATES
关键字：Concept factorization; Sparsity; Noise; Clustering
摘要：The concept factorization algorithm has received widespread attention and achieved remarkable results in the field of clustering. However, when modeling this clustering algo- rithm, it is necessary to initialize two new low-dimensional matrices that are independent of the objective matrix and continuously approximate the objective matrix through alter- nating iterative updating, thus inevitably introducing some noise factors that are undesir- able for the model. Especially in the objective function constructed by square loss, the noise factors have a more significant influence on the clustering performance. To solve this issue, a dual graph-regularized sparse concept factorization (DGSCF) algorithm is proposed in this paper. In addition to maintaining the geometric structure of the data using dual graph regularization, DGSCF adopts an optimization framework based on l1 and Frobenius norms, which enhance the ability of feature selection and sparsity to eliminate the influence of noise factors on the algorithm performance. The corresponding alternating iterative updat- ing rules and convergence proof of the DGSCF are provided. Finally, experiments on eight public datasets show its effectiveness and superiority.
合写作者：Ping Deng,Hongjun Wang,Pengfei Zhang
第一作者：Dexian Wang
论文类型：SCI
通讯作者：Tianrui Li
学科门类：工学
文献类型：J
卷号：607
页面范围：1074–1088
ISSN号：0020-0255
是否译文：否
发表时间：2022-05-31
收录刊物：SCI