Graph Learning for Multiview Clustering

By Kun Zhan, Changqing Zhang, Junpeng Guan, Junsheng Wang. IEEE Transactions on Cybernetics, 2018

429 citations recorded by the source corpus.

Most existing graph-based clustering methods need a predefined graph and their clustering performance highly depends on the quality of the graph. Aiming to improve the multiview clustering performance, a graph learning-based method is proposed to improve the quality of the graph. Initial graphs are learned from data points of different views, and the initial graphs are further optimized with a rank constraint on the Laplacian matrix. Then, these optimized graphs are integrated into a global graph with a well-designed optimization procedure. The global graph is learned by the optimization procedure with the same rank constraint on its Laplacian matrix. Because of the rank constraint, the cluster indicators are obtained directly by the global graph without performing any graph cut technique and the $k$ -means clustering. Experiments are conducted on several benchmark datasets to verify the effectiveness and superiority of the proposed graph learning-based multiview clustering algorithm comparing to the state-of-the-art methods.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/69e8690ade2e5f5d999f3bf96dbc156abb655b87

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yong Xu
2. Jie Wen
3. Hong Liu
4. Yi Yang
5. Kun Zhan
6. Feiping Nie
7. Jing Wang
8. Lifang He

In Statistical and Nonlinear Physics

1. Yi Yang
2. Feiping Nie
3. Jing Wang
4. Kun Zhan
5. Lifang He
6. Xuelong Li

Citations in the corpus

• Multiview Consensus Graph Clustering (2019, methodology): “In [11], Eq.”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, background): “Thus exploiting the multiview features has the potential to improve the performance of different machine learning tasks [6-12].”
• Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, methodology): “subdivided into three subgroups, including subspace methods [6,18,24], matrix factorization methods [45,53,56], and graph methods [28, 55, 60].”