Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.

中文翻译：

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自加权多视图模糊聚类

由于每个视图中的数据可能包含与其他视图不同的不同信息，并且在多视图学习中具有所有视图的公共信息，因此设计了许多多视图聚类方法来使用这些信息（包括每个视图的不同信息）以及所有视图的公共信息）以提高聚类性能。然而，以前的多视图聚类方法不能有效地检测这些信息，从而难以输出可靠的聚类模型。在本文中，我们提出了一种模糊、稀疏和鲁棒的多视图聚类方法来考虑数据之间的各种关系（如视图重要性、视图稳定性和视图多样性），该方法可以有效地提取不同信息和共同信息。信息以及平衡这两种信息。而且，我们设计了一种交替优化算法来求解得到的目标函数，并证明我们提出的算法实现了快速收敛。值得注意的是，现有的多视图聚类方法只考虑部分关系，因此是我们提出的框架的一个特例。在合成数据集和真实数据集上的实验结果表明，我们提出的方法在聚类精度、归一化互信息、纯度和调整后的 rand 指数等聚类评估指标方面优于最先进的聚类方法。因此是我们提出的框架的一个特例。在合成数据集和真实数据集上的实验结果表明，我们提出的方法在聚类精度、归一化互信息、纯度和调整后的 rand 指数等聚类评估指标方面优于最先进的聚类方法。因此是我们提出的框架的一个特例。在合成数据集和真实数据集上的实验结果表明，我们提出的方法在聚类精度、归一化互信息、纯度和调整后的 rand 指数等聚类评估指标方面优于最先进的聚类方法。