Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization

TL;DR

This paper introduces a novel multi-view clustering method using non-negative tensor factorization of anchor graph tensors, improving interpretability and inter-view information integration for large-scale data clustering.

Contribution

It proposes a new anchor graph tensor factorization approach that enhances interpretability and captures inter-view relationships in multi-view clustering.

Findings

Outperforms existing methods in clustering accuracy.

Improves interpretability of clustering results.

Effectively integrates multi-view information.

Abstract

The clustering method based on the anchor graph has gained significant attention due to its exceptional clustering performance and ability to process large-scale data. One common approach is to learn bipartite graphs with K-connected components, helping avoid the need for post-processing. However, this method has strict parameter requirements and may not always get K-connected components. To address this issue, an alternative approach is to directly obtain the cluster label matrix by performing non-negative matrix factorization (NMF) on the anchor graph. Nevertheless, existing multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix and often overlook the inter-view information. We address this limitation by using non-negative tensor factorization to decompose an anchor graph tensor that combines anchor graphs from multiple views. This approach allows us to consider inter-view information comprehensively. The decomposed tensors, namely the sample indicator tensor and the anchor indicator tensor, enhance the interpretability of the factorization. Extensive experiments validate the effectiveness of this method.