Bipartite Graph based Multi-view Clustering (Extended Abstract)
Document Type
Conference Proceeding
Date of Original Version
1-1-2023
Abstract
In existing graph-based multi-view clustering algorithms, consensus cluster structures are explored by constructing similarity graphs of multiple views and then fusing them into a unified superior graph. However, they overlook consensus information when learning each graph independently, resulting in the undesirable unified graph with biases. To this end, we proposed a framework named bipartite graph based multi-view clustering (BIGMC) in [1] to tackle this challenge. To summarize, the key idea of BIGMC is to employ a small number of uniform anchors to represent the consensus information across views. In this way, BIGMC creates a bipartite graph between data points and anchors for each view, which are then fused to generate a unified bipartite graph. The unified graph would in turn improve each view bipartite graph and the anchor set. Finally, the clusters are formed directly using the unified graph. In this extended abstract, we also summarize the effectiveness of BIGMC as shown in experimental results originally presented in [1].
Publication Title, e.g., Journal
Proceedings International Conference on Data Engineering
Volume
2023-April
Citation/Publisher Attribution
Li, Lusi, and Haibo He. "Bipartite Graph based Multi-view Clustering (Extended Abstract)." Proceedings International Conference on Data Engineering 2023-April, (2023). doi: 10.1109/ICDE55515.2023.00371.