论文标题
通过观点进化方案不平衡的多视图聚类不平衡：弱视图是肉；强烈的景色吃
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
论文作者
论文摘要
不完整的多视图聚类是处理现实世界不完整的多视图数据的重要技术。以前的作品假定所有视图都具有相同的不完整性，即平衡不完整。但是，不同的观点通常具有明显的不完整，即不平衡的不完整，这会导致强烈的观点（低分辨率的观点）和薄弱的观点（高度完整的视图）。不平衡的不完整使我们无法直接使用先前的聚类方法。在本文中，受到有效的生物进化论的启发，我们将新型的观点进化方案设计为群集强烈和弱的观点。此外，我们提出了一种不平衡的不完整的多视图聚类方法（UIMC），这是基于视图进化的第一个有效方法，用于不平衡的不完整多视图群集。与以前的方法相比，UIMC具有两个独特的优势：1）提出了加权的多视图子空间聚类以整合这些不平衡的不完整视图，从而有效地解决了不平衡的不完整的多视图问题； 2）它设计了低级别和健壮的表示以恢复数据，从而减少了不完整和噪音的影响。广泛的实验结果表明，与其他最新方法相比，在三个评估指标上，UIMC在三个评估指标上提高了高达40％的群集性能。
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using the previous methods for clustering. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate these unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view problem; 2) it designs the low-rank and robust representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods.
