Double-Ended Joint Learning for Multi-View Clustering

Du Liang; Li Xiaodong; Chen Yan; Zhou Peng; Qian Yuhua

doi:10.7544/issn1000-1239.202440175

Journal of Computer Research and Development > 2025 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440175 CSTR: 32373.14.issn1000-1239.202440175

Du Liang, Li Xiaodong, Chen Yan, Zhou Peng, Qian Yuhua. Double-Ended Joint Learning for Multi-View Clustering[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440175

Citation:

Du Liang, Li Xiaodong, Chen Yan, Zhou Peng, Qian Yuhua. Double-Ended Joint Learning for Multi-View Clustering[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440175

Citation:

Du Liang, Li Xiaodong, Chen Yan, Zhou Peng, Qian Yuhua. Double-Ended Joint Learning for Multi-View Clustering[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440175

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Double-Ended Joint Learning for Multi-View Clustering

1.
School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2.
College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong, Shanxi, 030600
3.
School of Computer Science and Technology, Anhui University, Hefei 230601
4.
Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006

Funds: This work was supported by the National Natural Science Foundation of China (62376146,62176001), the Shanxi Province Central Guidance for Local Science and Technology Development Special Projec (YDZJSX20231D003), the Outstanding Young Science Program of Anhui Higher Education Institustions (2023AH030004), the Key Research and Development Program of Shanxi Province（202202020101004）, and the Natural Science Foundation of Shanxi Province (20210302123455).

More Information

Author Bio:
Du Liang: born in 1985. PhD, associate professor, PhD supervisor, and member of CCF. His main research interests include unsupervised learning and machine learning

Li Xiaodong: born in 1999. Master and member of CCF. His main research interests include unsupervised learning

Chen Yan: born in 1994. PHD and lecture. Her main research interests include unsupervised learning

Zhou Peng: born in 1989. PhD, associate professor, PhD supervisor, and member of CCF. His main research areas are machine learning, data mining

Qian Yuhua: born in 1976. Professor and PhD supervisor. His main research interests include pattern recognition, feature selection, rough set theory, granular computing，and artificial intelligence
Received Date: March 15, 2024
Revised Date: August 18, 2024
Accepted Date: January 08, 2025
Available Online: January 08, 2025

Graphical Abstract

Abstract

Abstract

Multi-anchor graph approaches have attracted more and more attention for their potential in addressing the challenges of large-scale multi-view clustering. However, existing methods leveraging multi-anchor graphs encounter several hurdles when it comes to tackling this challenge. The consistency-anchored graph learning methods struggle with handling misaligned anchor graphs and necessitates additional post-processing with consistency graph, thereby constraining the accuracy and reliability of clustering outcomes. And the anchor graph ensemble clustering method fails to harness the complementary information from different views during the independent generation of candidate base clustering and overlooks the original anchor graphs during fusion, thus impacting the effectiveness and stability of clustering results. To address these challenges, we propose a novel approach based on double-ended joint learning for multi-view clustering. The method fully considers the duality between multi-anchor information and samples in multi-anchor graphs, achieving synchronized clustering between anchor-end and sample-end. Moreover, under the guidance of multi-anchor information, it achieves joint alignment between sample-end clustering and multiple anchor-end clustering. Unlike existing methods, the approach does not require direct learning of consistent anchor graph, thus can handle any type of anchor misalignment issues and mitigating the negative impact of separate graph learning and partitioning on clustering performance. Additionally, it utilizes multiple anchor graphs for anchor-end clustering and sample-end clustering within a unified optimization framework, effectively addressing the limitations of base clustering and the ensemble stage in leveraging multiple anchor graphs. Experimental results demonstrate that the proposed method outperforms several comparative methods in terms of clustering performance and time consumption, effectively enhancing the clustering performance of multi-view data. The relevant code for the proposed method and comparative methods is provided in the supplementary material: http://github.com/lxd1204/DLMC.
- multi-view clustering,
- anchor end,
- sample end,
- alignment,
- ensemble

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References (34)

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