聚类分析
拉普拉斯矩阵
光谱聚类
图形
计算机科学
数学
理论计算机科学
数据挖掘
人工智能
作者
Kun Zhan,Feiping Nie,Jing Wang,Yi Yang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2019-03-01
卷期号:28 (3): 1261-1270
被引量:255
标识
DOI:10.1109/tip.2018.2877335
摘要
A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly connected components where is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as -means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.
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