Anchors Bring Stability and Efficiency: Fast Tensorial Multi-view Clustering on Shuffled Datasets

Tensorial Multi-view Clustering (TMC) methods have received significant attention due to their ability to capture the high-order correlation among different views. Despite their notable progress, two issues persist: 1) Most TMC methods are highly reliant on prior structure information in sorted datasets, rendering them challenging to handle the shuffled case. 2) Extremely high computational complexity arising from tensor-related operations. To address these limitations, we propose a novel framework termed Anchors Bring Stability and Efficiency: Fast Tensorial Multi-view Clustering on Shuffled Case (SE-FTMC). SE-FTMC first learns a set of anchors and a unified corresponding matrix between anchors and samples, then the low-rank tensor learning is adopted in anchor space instead of sample space to avoid reliance on the prior information hidden in sorted samples and reduce the computational complexity. Finally, SE-FTMC passes the high-order correlations learned in the anchor space into the sample space through the corresponding matrix to improve the efficiency and stability of clustering. Furthermore, SE-FTMC is solved by an efficient algorithm with linear complexity. Extensive experiments on various datasets demonstrate the effectiveness and superiority of our SE-FTMC compared with state-of-the-art methods. The code is publicly available at: https://github.com/jijintian/SE-FTMC.