S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
CVPR 2024(2024)
摘要
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.
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