核心概念
Proposing a novel tensor-based graph learning framework for multi-view clustering that considers consistency and specificity.
要約
The content introduces a novel tensor-based graph learning framework for multi-view clustering. It addresses the limitations of existing methods by focusing on consistency and specificity in graph learning. The proposed method utilizes the Stiefel manifold for similarity distance measurement and formulates a tensor graph fusion framework. Experimental results demonstrate superior performance over state-of-the-art methods.
統計
Experiments on real-world datasets have shown superior performance.
The proposed method achieves a perfect score of 100% in ACC, NMI, ARI, and Fscore on the HW dataset.
The accuracy of the proposed method on the 3-sources dataset is 77.57%.
The proposed method achieves an accuracy of 76.97% on the Yale dataset.
The proposed method achieves an accuracy of 77.33% on the WebKB dataset.
引用
"We propose a novel tensor-based graph learning method, namely Tensor-based Graph Learning with Consistency and Specificity (CSTGL)."
"Experiments on real-world datasets have demonstrated that CSTGL outperforms some state-of-art multi-view clustering methods."