SimMTC: Simple Multi-view Tensor Clustering

Tensor-based multi-view clustering algorithms have attracted considerable attention due to their superior clustering performance. However, these algorithms typically treat each view independently, failing to utilize the complementary information across all views, thus lacking globality. Additionally, employing low-rank tensor constraints to extract consistent information among views may result in the loss of important information due to weak consistency constraints. These limitations significantly hinder the clustering performance. To address these issues, we propose Simple Multi-view Tensor Clustering (SimMTC), which achieves globality and strong consistency. SimMTC first applies Fast Fourier Transform (FFT) to the bipartite graphs to obtain high-frequency and low-frequency information, which encode similarities between samples and anchors from all views, thereby capturing global information. Orthogonal tensor factorization is then conducted in the frequency domain. Moreover, a novel strong consistency constraint based on FFT is introduced, which enhances the extraction of consistent information in the frequency domain. What's more, an efficient alternative optimization algorithm is designed to solve the optimization problem in SimMTC. Finally, extensive experiments on real-world datasets demonstrate that SimMTC achieves state-of-the-art clustering performance.