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Multi-View Comprehensive Graph Clustering
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AI
DOI：10.1109/TCE.2023.3255231.png)
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Abstract
En 中文
Multi-view clustering algorithms have been successfully used in different consumer electronic products, such as common digital cameras and unmanned vehicles. Currently, existing multi-view graph clustering (MVGC) methods learn the similarity of directly connected samples for clustering. However, these MVGC methods can not fully consider the indirect relation among samples and high-order relation across multi-view data. In this paper, a new multi-view comprehensive graph clustering (MCGC) method is devised, which can fully learn the similarity based on (1) first-order proximity (FOP) (i.e., the direct relation of pairwise samples); (2) second-order proximity (SOP) (i.e., the indirect relation of pairwise samples); and (3) third-order proximity (TOP) (i.e., the three-order relation of multiple views). Since the operations of these three components are iteratively carried out, the interaction between similarity learning can be encouraged and the comprehensive graph can be generated effectively for clustering. In-depth experiments on six commonly benchmark datasets show the superiority of the MCGC method.
Keywords:
Tensors
Excavation
Correlation
Consumer electronics
Clustering methods
Clustering algorithms
Merging
Comprehensive graph learning
multi-view clustering
similarity learning
low-rank tensor representation
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10.9
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5.1K
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6.8K