论文标题
通过自适应视图融合的双重自加权多视图聚类
Double Self-weighted Multi-view Clustering via Adaptive View Fusion
论文作者
论文摘要
多视图群集已应用于许多现实世界中原始数据通常包含噪音的现实应用程序。已经提出了一些基于图的多视图聚类方法来尝试减少噪声的负面影响。但是，即使存在冗余功能或噪声，也以前基于图的多视图聚类方法同样对所有功能进行处理，这显然是不合理的。在本文中，我们提出了一个新颖的多视图聚类框架，双重自加权多视图聚类（DSMC）来克服上述缺陷。 DSMC执行双重自加权操作，以删除每个图中的冗余功能和噪音，从而获得可靠的图形。对于第一个自加权操作，它通过引入自适应权重矩阵将不同的权重分配给不同的特征，该矩阵可以强化重要特征在关节表示中的作用，并使每个图表可靠。对于第二次自加权操作，它通过施加自适应权重因子来加权不同的图形，该系数可以将更大的权重分配给更健壮的图形。此外，通过设计自适应多个图融合，我们可以融合不同图中的特征以集成这些图以进行聚类。六个现实世界数据集的实验证明了其优势比其他最先进的多视图聚类方法。
Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view clustering methods have been proposed to try to reduce the negative influence of noises. However, previous graph-based multi-view clustering methods treat all features equally even if there are redundant features or noises, which is obviously unreasonable. In this paper, we propose a novel multi-view clustering framework Double Self-weighted Multi-view Clustering (DSMC) to overcome the aforementioned deficiency. DSMC performs double self-weighted operations to remove redundant features and noises from each graph, thereby obtaining robust graphs. For the first self-weighted operation, it assigns different weights to different features by introducing an adaptive weight matrix, which can reinforce the role of the important features in the joint representation and make each graph robust. For the second self-weighting operation, it weights different graphs by imposing an adaptive weight factor, which can assign larger weights to more robust graphs. Furthermore, by designing an adaptive multiple graphs fusion, we can fuse the features in the different graphs to integrate these graphs for clustering. Experiments on six real-world datasets demonstrate its advantages over other state-of-the-art multi-view clustering methods.
