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Consistent Multiple Graph Embedding for Multi-View Clustering
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DOI：10.1109/TMM.2021.3136098.png)
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Abstract
En 中文
Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based multi-view clustering, it is still challenging to fuse characteristics from various views to learn a common representation for clustering. In this paper, we propose a novel Consistent Multiple Graph Embedding Clustering framework (CMGEC). Specifically, a multiple graph auto-encoder (M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module (MMIM) is introduced. Furthermore, a graph fusion network (GFN) is devised to explore the relationship among graphs from different views and provide a common consensus graph needed in M-GAE. By jointly training these models, the common representation can be obtained, which encodes more complementary information from multiple views and depicts data more comprehensively. Experiments on three types of multi-view datasets demonstrate CMGEC outperforms the state-of-the-art clustering methods.
Keywords:
Mutual information
Clustering methods
Clustering algorithms
Data models
Task analysis
Laplace equations
Fuses
Graph neural networks
multi-view clustering
mutual information
representation learning
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