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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Multi-view clustering guided by unconstrained non-negative matrix factorization
影响因子：8.6
DOI码：10.1016/j.knosys.2023.110425
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; Multi-view clustering; Unconstrained; Element updates
摘要：Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages.
合写作者：Tianrui Li,Hong Peng,Shi-Jinn Horng
第一作者：Ping Deng
论文类型：SCI
通讯作者：Dexian Wang,Hongjun Wang
学科门类：工学
文献类型：J
卷号：266
页面范围：110425
ISSN号：0950-7051
是否译文：否
发表时间：2023-03-03
收录刊物：SCI