聚类分析
计算机科学
约束聚类
光谱聚类
约束(计算机辅助设计)
一致性(知识库)
人工智能
共识聚类
数据挖掘
完整信息
代表(政治)
正规化(语言学)
机器学习
相关聚类
数学
CURE数据聚类算法
几何学
数理经济学
政治
法学
政治学
作者
Jie Wen,Hui‐Jie Sun,Bob Zhang,Jinxing Li,Zheng Zhang,Bob Zhang
标识
DOI:10.1016/j.neunet.2020.10.014
摘要
Incomplete multi-view clustering which aims to solve the difficult clustering challenge on incomplete multi-view data collected from diverse domains with missing views has drawn considerable attention in recent years. In this paper, we propose a novel method, called consensus guided incomplete multi-view spectral clustering (CGIMVSC), to address the incomplete clustering problem. Specifically, CGIMVSC seeks to explore the local information within every single-view and the semantic consistent information shared by all views in a unified framework simultaneously, where the local structure is adaptively obtained from the incomplete data rather than pre-constructed via a k-nearest neighbor approach in the existing methods. Considering the semantic consistency of multiple views, CGIMVSC introduces a co-regularization constraint to minimize the disagreement between the common representation and the individual representations with respect to different views, such that all views will obtain a consensus clustering result. Experimental comparisons with some state-of-the-art methods on seven datasets validate the effectiveness of the proposed method on incomplete multi-view clustering.
科研通智能强力驱动
Strongly Powered by AbleSci AI