# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 2 Related Work  
Multi-view clustering is a very powerful data analysis tools for unsupervised learning of data with heterogeneous features. In the past two decades, many multi-view clustering methods have been proposed to achieve robust clustering performance. In the following, we will briefly introduce several multi-view clustering methods from different perspectives. Subspace-based methods have recently become the mainstay of multi-view clustering research, aiming to discover potential subspace structures across different views. For example, [PERSON] et al. [12] propose a multi-view subspace clustering method that utilizes a common cluster structure to exploit the consistency information across multiple views. [PERSON] et al. [13] propose a diversity-induced multi-view subspace clustering method that adopts the Hilbert-Schmidt independence criterion as the diversity term to explore the complementary information of multi-view data. [PERSON] et al. [14] propose a multi-view subspace clustering method that simultaneously considers the consistency and specificity for learning the subspace representation. [PERSON] et al. [15] propose a multi-view subspace clustering method that considers the complementarity of multi-view data by adopting a position-aware exclusivity term. [PERSON] et al. [16] propose a rank consistency induced multi-view subspace clustering model that learns a consistent subspace structure. [PERSON] and [PERSON] [17] propose a multi-view subspace clustering method that adopts an agreement term to ensure the consistency among these subspace representations. To capture the high-order correlations underlying multi-view data, the tensor technique is adopted to exploit the complementary information among different views. For example, [PERSON] et al. [18] propose a low-rank tensor constrained multi-view subspace clustering model that adopts a low-rank tensor constraint for the obtained subspace representations. [PERSON] et al. [19] utilize the subspace representations of multiple views as a tensor data and then utilize the tensor-singular value decomposition on the rotated tensor to guarantee the consensus among different views. [PERSON] et al. [20] propose a tensorized multi-view subspace representation learning that adopts a low-rank constraint model for the subspace representation tensor. [PERSON] et al. [21] propose a multi-view subspace clustering model by organizing the multi-view data as tensorial data, and the tensorial data can be represented by a \(t\)-linear combination with sparse and low-rank penalty. Recently, researchers considered partition-level multi-view information fusion and proposed a partition-based clustering model to construct joint optimization of multi-view subspace clustering. For example, [PERSON] et al. [22] propose a unified multi-view subspace clustering model that implements the graph construction, the generation of basic partitions, and the fusion of consensus clustering in an interactive way. [PERSON] et al. [23] propose a partition fusion-based multi-view subspace clustering method that utilizes the different partitions to find a shared partition. [PERSON] et al. [24] develop a consensus one-step multi-view subspace clustering method that fuses the subspace representation learning, partition learning, and clustering into a whole to iteratively optimize. [PERSON] et al. [25] propose to integrate multi-view information in the partition space and obtain clustering results by assigning each partition with a respective rotation matrix. Furthermore, each view is assigned a weight to consider the differences  
Figure 1: Framework of proposed LGOMSC. in the clustering capacity of the views. The anchor-based model is proposed to fit for the large-scale multi-view data. [PERSON] et al. [26] propose a large-scale multi-view subspace clustering method by integrating the anchor graphs from different views for spectral clustering. [PERSON] et al. [27] propose a fast parameter-free multi-view subspace clustering by adaptively learning the anchors and graph structure. [PERSON] et al.