Although many multi-view clustering (MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects, which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm (SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional human-readable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides, SRMVEC delivers effectiveness on benchmark datasets by out-performing some state-of-the-art methods.

中文翻译：

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多视图数据的稀疏重构证据聚类

尽管已经提出了许多性能可接受的多视图聚类 (MVC) 算法，但据我们所知，几乎所有算法都需要提供正确数量的聚类。此外，这些现有算法仅为多视图对象创建硬分区和模糊分区，这些分区通常位于多视图特征空间的高度重叠区域。采用硬划分和模糊划分忽略了对象分配中的模糊性和不确定性，可能导致性能下降。为了解决这些问题，我们提出了一种新颖的稀疏重建多视图证据聚类算法（SRMVEC）。基于稀疏重建过程，SRMVEC 学习跨视图的共享亲和力矩阵，并通过为每个对象计算 2 个新定义的数学指标，将多视图对象映射到二维人类可读图表。从该图表中，用户可以检测聚类的数量，并选择数据集中存在的多个对象作为聚类中心。然后，SRMVEC在证据理论框架下推导了信用划分，提高了聚类的容错能力。消融研究显示了采用稀疏重建程序和证据理论的好处。此外，SRMVEC 通过超越一些最先进的方法，在基准数据集上提供了有效性。