Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works have proposed ways to handle this problem, but all of them fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is difficult to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address this issue. Specifically, the method maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the given views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. In addition, we design a three-step iterative algorithm to solve the resultant problem with linear complexity and proven convergence. Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches. The code is publicly available at https://github.com/wanxinhang/FCMVC-IV .

中文翻译：

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不完整视图的快速连续多视图聚类

多视图聚类（MVC）由于其能够利用跨视图的一致和互补信息而引起了广泛的关注。本文重点讨论 MVC 中的一个具有挑战性的问题，称为不完整连续数据问题 (ICDP)。具体来说，大多数现有算法假设视图是提前可用的，并且忽略了视图的数据观察随着时间的推移而累积的场景。由于隐私考虑或内存限制，在这些情况下无法存储以前的视图。一些作品提出了处理这个问题的方法，但它们都未能解决不完整的观点。 MVC 中的这种不完整连续数据问题（ICDP）很难解决，因为连续数据的不完整信息增加了在视图之间提取一致和互补知识的难度。我们提出具有不完整视图的快速连续多视图聚类（FCMVC-IV）来解决这个问题。具体来说，该方法维护一个可扩展的共识系数矩阵，并使用传入的不完整视图更新其知识，而不是存储和重新计算所有数据矩阵。考虑到给定的视图不完整，新收集的视图可能包含尚未出现的样本；开发了两个指标矩阵和一个旋转矩阵来匹配不同维度的矩阵。此外，我们设计了一种三步迭代算法来解决具有线性复杂度和经过验证的收敛性的结果问题。对各种数据集进行的综合实验证明了 FCMVC-IV 相对于竞争方法的优越性。该代码可在以下位置公开获取：https://github.com/wanxinhang/FCMVC-IV 。