GMC: Graph-Based Multi-View Clustering
By Hao Wang, Yan Yang, Bing Liu. IEEE Transactions on Knowledge and Data Engineering, 2020
805 citations recorded by the source corpus.
Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/6bd42802575f68498553cd92b0d4295d962c69e0
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Citations in the corpus
- Multi-view Contrastive Graph Clustering (2021, positive): “Graph-based multi-view clustering constructs a graph for each view and fuses them based on a weighting mechanism [Wang et al., 2019].”
- Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview (2021, neutral): “, SIFT, GIST, LBP, HoG, and Garbor [40], [41]; a document can be translated into different languages [42], [43].”