S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering

TL;DR

This paper introduces S^2MVTC, a scalable multi-view tensor clustering method that effectively captures correlations within and across views, achieving superior performance and efficiency on large datasets.

Contribution

The paper proposes a novel tensor low-frequency approximation operator and a tensor stacking approach for scalable multi-view clustering, emphasizing correlation learning and semantic consistency.

Findings

Outperforms state-of-the-art algorithms in clustering accuracy

Reduces CPU time significantly on large datasets

Demonstrates effectiveness on six large-scale multi-view datasets

Abstract

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.