Incomplete Multi-View Clustering Algorithm with Adaptive Graph Fusion

doi:10.3778/j.issn.1002-8331.2201-0192

Abstract

Abstract: Multi-view clustering can make full use of the consistency and difference of samples between different views, which has attracted more and more attention. The traditional multi-view clustering method assumes that the samples of each view are complete, however, the collected multi-views data are usually incomplete in practical applications. In order to perform clustering analysis on incomplete multi-view data, an incomplete multi-view clustering algorithm with adaptive graph fusion（IMC_AGF） is proposed. In IMC_AGF, the shared samples between two views are used as the anchors to construct a sample-sample similarity matrix by learning its consistency knowledge. Then the complementarity between two views are exploited to integrate all the similarity maps with adaptive graph fusion method, and the final similarity matrix of incomplete multi-view data is obtain. Finally, the spectral clustering is used to get the clustering result. The experimental results show that the proposed algorithm is superior to the classical multi-view clustering method.

Key words: incomplete multi-view clustering, adaptive graph fusion, anchors, similarity matrix, clustering algorithm

摘要： 多视图聚类能充分利用不同视图间数据的一致性和差异性，引起越来越多的关注。传统多视图聚类方法假设每个视图的数据都是完整的，然而在实际应用中，收集到的多视图数据常存在部分视图缺失的样本。为了对缺失多视图数据进行聚类分析，提出自适应图融合的缺失多视图聚类算法（IMC_AGF）。算法以两两视图间共有样本为瞄点构建样本-样本的相似度矩阵，学习其一致性知识，再利用两两视图间的互补性，用自适应图融合算法整合所有的相似度图，获取缺失多视图数据完整的相似度矩阵，然后进行谱聚类得到分类结果。实验结果表明，提出的算法优于与之比较的经典缺失多视图聚类方法。

关键词: 缺失多视图聚类, 自适应图融合, 瞄点, 相似度矩阵, 聚类算法

HUANG Zhanpeng, WU Jiekang, YI Faling. Incomplete Multi-View Clustering Algorithm with Adaptive Graph Fusion[J]. Computer Engineering and Applications, 2023, 59(9): 176-181.

黄展鹏, 吴杰康, 易法令. 自适应图融合的缺失多视图聚类算法[J]. 计算机工程与应用, 2023, 59(9): 176-181.

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