基于一步张量学习的多视图子空间聚类

A surge of the existing multi-view subspace clustering algorithms generally learn the third-order tensor representation first and then fuse the learned representation tensor into a unified affinity matrix. However, since they learn the representation tensor and the affinity matrix independently, they cannot seamlessly capture their high-order correlation. To address this challenge, we propose a novel multi-view subspace clustering method based on one-step tensor learning (OTSC) to jointly learn the representation tensor and affinity matrix. Specifically, we impose the low-rank tensor constraint on the representation tensor to explore the correlation of high-order cross-views dexterously, utilize the adaptive nearest neighbor strategy to reconstruct a flexible affinity matrix, and adopt the alternating direction method of multipliers (ADMM) to optimize our model. Extensive experiments on real multi-view data demonstrated the superiority of OTSC compared to the state-of-the-art methods.