View-shuffled clustering via the modified Hungarian algorithm

In the majority of existing multi-view clustering methods, the prerequisite is that the data have the correct cross-view correspondence. However, this strong assumption may not always hold in real-world applications, giving rise to the so-called View-shuffled Problem (VsP). To address this challenge, we propose a novel multi-view clustering method, namely View-shuffled Clustering via the Modified Hungarian Algorithm (VsC-mH). Specifically, we first establish the cross-view correspondence of the shuffled data utilizing strategies of the global alignment and modified Hungarian algorithm (mH) based intra-category alignment. Subsequently, we generate the partition of the aligned data employing matrix factorization. The fusion of these two processes facilitates the interaction of information, resulting in improved quality of both data alignment and partition. VsC-mH is capable of handling the data with alignment ratios ranging from 0 to 100%. Both experimental and theoretical evidence guarantees the convergence of the proposed optimization algorithm. Extensive experimental results obtained on six practical datasets demonstrate the effectiveness and merits of the proposed method.