S2MVTC: A Simple yet Efficient Scalable Multi-View Tensor Clustering

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 (S2MVTC) 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 S2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time especially when handling massive data. The code of S2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.