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Bipartite Graph Based Multi-View Clustering
IEEE Transactions on Knowledge and Data Engineering, 2022For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices of multiple views and then fuse them into a unified superior graph ...
Lusi Li, Haibo He
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Large-Scale Clustering With Structured Optimal Bipartite Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023The widespread arising of data size gives rise to the necessity of undertaking large-scale data clustering tasks. To do so, the bipartite graph theory is frequently applied to design a scalable algorithm, which depicts the relations between samples and a
Han Zhang, F. Nie, Xuelong Li
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Multiview Spectral Clustering With Bipartite Graph
IEEE Transactions on Image Processing, 2022Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction or ...
Haizhou Yang +4 more
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Bipartite Graph-based Discriminative Feature Learning for Multi-View Clustering
ACM Multimedia, 2022Multi-view clustering is an important technique in machine learning research. Existing methods have improved in clustering performance, most of them learn graph structure depending on all samples, which are high complexity.
Weiqing Yan +4 more
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Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering
ACM Transactions on Knowledge Discovery from Data, 2022Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result.
Peng Zhou +3 more
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Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection
IEEE International Conference on Computer Vision, 2021Domain Adaptive Object Detection (DAOD) relieves the reliance on large-scale annotated data by transferring the knowledge learned from a labeled source domain to a new unlabeled target domain.
Chaoqi Chen +5 more
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Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms ...
Xuelong Li, Han Zhang, Rong Wang, F. Nie
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Approximately Counting Butterflies in Large Bipartite Graph Streams
IEEE Transactions on Knowledge and Data Engineering, 2021Bipartite graphs widely exist in real-world scenarios and model binary relations like host-website, author-paper, and user-product. In bipartite graphs, a butterfly (i.e., $2\times 2$2×2 bi-clique) is the smallest non-trivial cohesive structure and plays
Rundong Li +7 more
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Learning an Optimal Bipartite Graph for Subspace Clustering via Constrained Laplacian Rank
IEEE Transactions on Cybernetics, 2021In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and ...
F. Nie, Wei Chang, Rong Wang, Xuelong Li
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Fuzzy Embedded Clustering Based on Bipartite Graph for Large-Scale Hyperspectral Image
IEEE Geoscience and Remote Sensing Letters, 2021Hyperspectral image (HSI) clustering has been widely used in the field of remote sensing. However, most traditional clustering algorithms are not suitable for dealing with large-scale HSI due to their low clustering performance and high computational ...
Xiaojun Yang +4 more
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