Current multi-view clustering algorithms use multistage strategies to conduct clustering, or require cluster number or similarity matrix prior, or suffer influence of irrelevant features and outliers. In this paper, we propose a Joint Robust Multi-view (JRM) spectral clustering algorithm that considers information from all views of the multi-view dataset to conduct clustering and solves the issues, such as initialization, cluster number determination, similarity measure, feature selection, and outlier reduction around clustering, in a unified way. The optimal performance could be reached when all views are considered and the separated stages are combined in a unified way. Experiments have been performed on six real-world benchmark datasets and our proposed JRM algorithm outperforms the comparison clustering algorithms in terms of two evaluation metrics for clustering algorithms including accuracy and purity.

当前的多视图聚类算法使用多阶段策略来进行聚类，或者事先需要聚类数或相似度矩阵，或者受到不相关特征和离群值的影响。在本文中，我们提出了一种联合鲁棒多视图（JRM）谱聚类算法，该算法考虑来自多视图数据集所有视图的信息来进行聚类，并解决诸如初始化，聚类数确定，相似性度量，特征之类的问题。选择，并以统一的方式减少聚类的异常值。当考虑所有视图并将分离的阶段以统一的方式组合时，可以达到最佳性能。