主动三支聚类下的全局和局部多视角多标签学习算法

doi:10.6040/j.issn.1672-3961.0.2020.234

摘要/Abstract

摘要： 为了考虑样本与簇之间不确定的归属关系并衍生全局和局部多视角多标签学习的应用范围,提岀一个主动三支聚类下的全局和局部多视角多标签学习算法(global and local multi-view multi-label learning machine with active three-way clustering, GLMVML-ATC)。通过使用主动三支聚类,样本是否归属于一个簇将取决于不确定样本属于核心区域的概率。这使得局部标签关联更可信,能够增强多视角多标签学习机的性能,并加速他们的发展。试验表明,GLMVML-ATC使得分类性能至少提升3%,增加的训练时间不超过7%,更优于典型的多视角、多标签学习机。

关键词: 三支聚类, 多视角, 多标签, 标签关联, 全局和局部

Abstract: In order to consider the uncertain belongingness relationship between instances and clusters and then extend the application scopes of global and local multi-view multi-label learning, an algorithm of global and local multi-view multi-label learning machine with active three-way clustering(GLMVML-ATC)was proposed. With the usage of active three-way clustering strategy, the belongingness of instances to a cluster depended on the probabilities of uncertain instances belonging to core regions. This made local label correlations more authentic, which enhanced the performances of multi-view multi-label learning machines further and accelerated their development. Experimental results validated that GLMVML-ATC improved the classification performances with 3% at least, while the added training time less than 7%. It was superior to the classical multi-view learning machines and multi-label learning machines.

Key words: three-way clustering, multi-view, multi-label, label correlation, global and local

中图分类号:

TP391

朱昌明,岳闻,王盼红,沈震宇,周日贵. 主动三支聚类下的全局和局部多视角多标签学习算法[J]. 山东大学学报 (工学版), 2021, 51(2): 34-46.

ZHU Changming, YUE Wen, WANG Panhong, SHEN Zhenyu, ZHOU Rigui. Global and local multi-view multi-label learning with active three-way clustering[J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 34-46.

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