Consistent Multiple Graph Embedding for Multi-View Clustering

Understand

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.