Consistent Multiple Graph Embedding for Multi-View Clustering
- URL: http://arxiv.org/abs/2105.04880v1
- Date: Tue, 11 May 2021 09:08:22 GMT
- Title: Consistent Multiple Graph Embedding for Multi-View Clustering
- Authors: Yiming Wang, Dongxia Chang, Zhiqiang Fu and Yao Zhao
- Abstract summary: We propose a novel Consistent Multiple Graph Embedding Clustering framework(CMGEC)
Specifically, a multiple graph auto-encoder is designed to flexibly encode the complementary information of multi-view data.
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.
- Score: 41.17336912278538
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 remains
a challenge 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 latent 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.
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