Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning

The Unaligned Multi-view Clustering (UMC) problem is currently receiving widespread attention, focusing on clustering unaligned multi-view data generated in real-world applications. Although some algorithms have emerged to address this issue, there still exist the following drawbacks: 1) The fully unknown correspondence of samples across views can significantly limit the exploration of consistent clustering structure. 2) The fixed representation space makes it difficult to mine the comprehensive information in the original data. 3) Unbiased tensor rank approximation is desired to capture the high-order correlation among different views. To address these issues, we proposed a novel UMC framework termed Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning (TUMCR). Specifically, TUMCR designs a multi-scale representation learning and alignment framework, which constructs multi-scale representation spaces to comprehensively explore the unknown correspondence across views. Then, a tensorial multi-scale fusion module is proposed to fuse multi-scale representations and explore the high-order correlation hidden in different views, which utilizes the Enhanced Tensor Rank (ETR) to learn the low-rank structure. Furthermore, TUMCR is solved by an efficient algorithm with good convergence. Extensive experiments on different types of datasets demonstrate the effectiveness and superiority of our TUMCR compared with state-of-the-art methods. Our code is publicly available at: https://github.com/jijintian/TUMCR.