Multi-view spectral clustering is a hot research area which has attracted increasing attention. Most existing multi-view spectral clustering methods utilize a two-step strategy. The first step obtains a common embedding by fusing spectral embeddings of different views, and the second step conducts hard clustering, such as K-means or spectral rotation, on the common embedding. Because the goal of the first step is not obtaining optimal clustering result, and the requirement to post-processing makes the final clustering result uncertain. In this paper, we propose a novel one-step multi-view spectral clustering method, in which the spectral embedding and nonnegative embedding are unified into one framework. Therefore, our method can avoid the uncertainty brought by post-processing and obtain optimal clustering result. Moreover, the nonnegative embedding is divided into two parts. The common nonnegative embedding indicates the shared cluster structure, and the specific nonnegative embedding indicates the exclusive cluster structure of each view. Hence, our method can well tackle with noises and outliers of different views. Furthermore, an alternating iterative algorithm is used to solve the joint optimization problem. Extensive experimental results on four real-world datasets have demonstrated the effectiveness of the proposed method.

多视图光谱聚类是一个热门的研究领域，吸引了越来越多的关注。大多数现有的多视图光谱聚类方法都采用两步策略。第一步是通过融合不同视图的频谱嵌入来获得通用嵌入，第二步是对通用嵌入进行硬聚类，例如K均值或频谱旋转。由于第一步的目的不是获得最佳的聚类结果，并且对后处理的要求使得最终的聚类结果不确定。在本文中，我们提出了一种新颖的单步多视图光谱聚类方法，该方法将光谱嵌入和非负嵌入统一到一个框架中。因此，我们的方法可以避免后处理带来的不确定性，并获得最佳的聚类结果。而且，非负嵌入分为两部分。通用非负嵌入表示共享的群集结构，特定非负嵌入表示每个视图的互斥群集结构。因此，我们的方法可以很好地解决噪声和不同观点的离群值的问题。此外，使用交替迭代算法来解决联合优化问题。在四个真实世界的数据集上的大量实验结果证明了该方法的有效性。交替迭代算法用于解决联合优化问题。在四个真实世界的数据集上的大量实验结果证明了该方法的有效性。交替迭代算法用于解决联合优化问题。在四个真实世界的数据集上的大量实验结果证明了该方法的有效性。