论文标题
一致性感知和不一致 - 基于图形的多视图集群
Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering
论文作者
论文摘要
多视图数据分析已越来越受欢迎，因为在机器学习应用程序中经常遇到多视图数据。多视图数据聚类的一种简单但有前途的方法是多视图聚类（MVC），该方法已通过学习在多视图中共享的潜在共同特征，已广泛开发给给定的主题分为某些聚类组。在现有方法中，基于图的多视图聚类（GMVC）通过利用称为统一矩阵的共享图矩阵来实现最新性能。但是，包括GMVC在内的现有方法不会明确地解决输入图矩阵中不一致的部分。因此，它们受到不可接受的聚类性能的不利影响。为此，本文提出了一种新的GMVC方法，该方法结合了跨多个视图的一致和不一致的部分。该建议被指定为CI-GMVC。对现实世界数据集的数值评估证明了提出的CI-GMVC的有效性。
Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC.
