A Multi-View Unified Representation Learning Network for Subspace Clustering

Lin Yuxiu; Liu Hui; Yu Xiao; Zhang Caiming

doi:10.7544/issn1000-1239.202440431

Journal of Computer Research and Development > 2024 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440431

Lin Yuxiu, Liu Hui, Yu Xiao, Zhang Caiming. A Multi-View Unified Representation Learning Network for Subspace Clustering[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440431

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A Multi-View Unified Representation Learning Network for Subspace Clustering

Lin Yuxiu^{1, 2,},
Liu Hui^{1, 2, ,},
Yu Xiao^{1, 2},
Zhang Caiming^{2, 3}

1.
School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014
2.
Digital Media Technology Key Lab of Shandong Province(Shandong University of Finance and Economics), Jinan 250014
3.
School of Software, Shandong University, Jinan 250101

Funds: This work was supported by the National Natural Science Foundation of China (62072274, U22A2033), the Central Guidance on Local Science and Technology Development Project (YDZX2022009), and the Special Funds of Taishan Scholars Project of Shandong Province (tstp20221137).

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Author Bio:
Lin Yuxiu: born in 1996. PhD candidate. Her main research interests include data mining and visualization, and multi-view learning

Liu Hui: born in 1978. PhD, professor, PhD supervisor. Member of CCF. Her main research interests include machine learning, data mining and image processing

Yu Xiao: born in 1981. PhD, associate professor, master supervisor. Member of CCF. Her main research interests include statistical machine learning and data mining

Zhang Caiming: born in 1955. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include CAGD&CG, information visualization and image processing
Received Date: November 29, 2023
Revised Date: November 29, 2023
Accepted Date: October 14, 2024
Available Online: October 21, 2024

Graphical Abstract

Abstract

Abstract

Multi-view subspace clustering aims to explore rich information across views to guide the clustering process. The key lies in effectively learning the unified representation and subspace representation between views. Recently, deep clustering methods have achieved promising effects due to the powerful representation capability of neural networks. However, the multi-source heterogeneity inherent in multi-view data allows existing methods to encode each view independently with a unimodal encoder, increasing the number of model parameters and limiting the model's generalization capability. Besides, low-rank subspace representation has been shown to facilitate clustering performance, while traditional nuclear norm regularization does not consider the difference between different singular values, leading to biased estimation. To tackle these two problems, we propose a novel multi-view unified representation learning network (namely, MURLN) for subspace clustering. Specifically, MURLN first uses the Transformer as the encoder architecture, which projects different views into the low-dimensional feature space with the same mapping rule by sharing parameters. In addition, a weighted fusion strategy for intra-view samples is conducted to learn a unified representation rationally. Finally, the weighted Schatten p-norm is introduced as the low-rank constraint of the subspace representation matrix. Extensive experiments on seven multi-view datasets verify the effectiveness and superiority of our proposed method.
- multi-view subspace clustering,
- Transformer,
- weighted fusion,
- low-rank representation,
- weighted Schatten p-norm

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References (49)

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