Yang Liu, Shen Chenyang, Hu Qinghua, Jing Liping, Li Yingbo
IEEE Trans Image Process. 2019 Nov 15. doi: 10.1109/TIP.2019.2952696.
Multiview clustering explores complementary information among distinct views to enhance clustering performance under the assumption that all samples have complete information in all available views. However, this assumption does not hold in many real applications, where the information of some samples in one or more views may be missing, leading to partial multiview clustering problems. In this case, significant performance degeneration is usually observed. A collection of partial multiview clustering algorithms has been proposed to address this issue and most treat all different views equally during clustering. In fact, because different views provide features collected from different angles/feature spaces, they might play different roles in the clustering process. With the diversity of different views considered, in this study, a novel adaptive method is proposed for partial multiview clustering by automatically adjusting the contributions of different views. The samples are divided into complete and incomplete sets, while a joint learning mechanism is established to facilitate the connection between them and thereby improve clustering performance. More specifically, the method is characterized by a joint optimization model comprising two terms. The first term mines the underlying cluster structure from both complete and incomplete samples by adaptively updating their importance in all available views. The second term is designed to group all data with the aid of the cluster structure modeled in the first term. These two terms seamlessly integrate the complementary information among multiple views and enhance the performance of partial multiview clustering. Experimental results on real-world datasets illustrate the effectiveness and efficiency of our proposed method.
多视图聚类探索不同视图之间的互补信息，以在所有样本在所有可用视图中都具有完整信息的假设下提高聚类性能。然而，在许多实际应用中，这种假设并不成立，其中一个或多个视图中某些样本的信息可能会缺失，从而导致部分多视图聚类问题。在这种情况下，通常会观察到显著的性能退化。已经提出了一系列部分多视图聚类算法来解决这个问题，并且大多数算法在聚类过程中平等对待所有不同的视图。事实上，由于不同的视图提供了从不同角度/特征空间收集的特征，它们在聚类过程中可能发挥不同的作用。考虑到不同视图的多样性，在本研究中，提出了一种新颖的自适应方法，通过自动调整不同视图的贡献来进行部分多视图聚类。样本被分为完整集和不完整集，同时建立了一种联合学习机制来促进它们之间的联系，从而提高聚类性能。更具体地说，该方法的特点是一个包含两个项的联合优化模型。第一项通过在所有可用视图中自适应地更新完整样本和不完整样本的重要性，从两者中挖掘潜在的聚类结构。第二项旨在借助第一项中建模的聚类结构对所有数据进行分组。这两个项无缝整合了多个视图之间的互补信息，提高了部分多视图聚类的性能。在真实世界数据集上的实验结果说明了我们提出的方法的有效性和效率。
