Related papers: Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization

Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization

URL: http://arxiv.org/abs/2404.00883v1
Date: Mon, 1 Apr 2024 03:23:55 GMT
Title: Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
Authors: Jing Li, Quanxue Gao, Cheng Deng, Qianqian Wang, Ming Yang,
Abstract summary: Multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix. We address this limitation by using non-negative tensor factorization to decompose an anchor graph tensor that combines anchor graphs from multiple views.
Score: 78.22249047957939
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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