Jiang Tianyu, Gao Quanxue
State Key Laboratory of Integrated Services Networks, Xidian University, Shaanxi 710071, China.
State Key Laboratory of Integrated Services Networks, Xidian University, Shaanxi 710071, China.
Neural Netw. 2022 Nov;155:348-359. doi: 10.1016/j.neunet.2022.08.027. Epub 2022 Sep 2.
Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.
基于图的多视图聚类由于在表征多媒体数据之间的复杂结构和关系方面具有高效性，已成为一个活跃的研究课题。然而，现有方法存在以下缺点：（1）由于图构建和特征分解，它们在大规模图学习中效率低下甚至失败。（2）它们不能很好地利用不同视图图中嵌入的互补信息和空间结构。为了充分利用互补信息并解决困扰基于图的多视图聚类的可扩展性问题，我们通过少量锚点和张量Schatten p范数最小化提出了一种高效的多图学习模型。具体来说，我们为每个视图通过锚图构建一个隐藏且易于处理的大图，并通过张量Schatten p范数正则化充分利用不同视图锚图中嵌入的互补信息。最后，我们开发了一种与数据大小成线性比例的高效算法来求解我们提出的模型。在几个数据集上的大量实验结果表明，我们提出的方法优于一些现有的多视图聚类算法。
