Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
This work addresses interpretability and inter-view integration in multi-view clustering, which is an incremental improvement for data analysis applications.
The paper tackles the problem of limited interpretability and overlooked inter-view information in multi-view clustering based on anchor graph factorization by proposing a method using non-negative tensor factorization on an anchor graph tensor, which enhances interpretability and comprehensively considers inter-view information, with extensive experiments validating its effectiveness.
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.