Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), the data is collected from different sensors and stored in distributed devices, these data can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information and increases the computational burden for subsequent tasks. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multi-view. Different views are considered to have equal contributions for clustering. To efficiently address the above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, adaptive graph learning automatically assigns weights to obtain a consensus graph. Meanwhile, the samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-art methods.