S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
This addresses the need for efficient clustering of massive multi-view datasets, offering an incremental improvement over existing anchor-based methods.
The paper tackles the problem of large-scale multi-view clustering by proposing S^2MVTC, which learns correlations of embedding features within and across views using a tensor low-frequency approximation operator, resulting in significantly outperforming state-of-the-art algorithms in clustering performance and CPU execution time on six datasets.
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper, we propose a simple yet efficient scalable multi-view tensor clustering (S^2MVTC) approach, where our focus is on learning correlations of embedding features within and across views. Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it. Additionally, we build a novel tensor low-frequency approximation (TLFA) operator, which incorporates graph similarity into embedding feature learning, efficiently achieving smooth representation of embedding features within different views. Furthermore, consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S^2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time, especially when handling massive data. The code of S^2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.