GMC: Graph-Based Multi-View Clustering

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 objec...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 32; H. 6; S. 1116 - 1129
Hauptverfasser: Wang, Hao, Yang, Yan, Liu, Bing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Computational complexity
Computational modeling
data fusion
Data points
graph-based clustering
Kernel
Laplace equations
Laplacian matrix
Learning
Matrix converters
Multi-view clustering
Optimization
rank constraint
ISSN:1041-4347, 1558-2191
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Zusammenfassung: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 G raph-based M ulti-view C lustering (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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2019.2903810