Abstract
Deep subspace clustering methods use deep neural networks to project input data into the latent space, leveraging the inherent self-expressiveness (SE) properties of the data as a similarity metric to handle high-dimensional data effectively. However, existing methods focus solely on the SE relationships within the latent space, which constrains their capacity to capture subspace structures. To overcome this limitation, we introduce a novel deep subspace clustering method using dual self-expressiveness and convolutional fusion (DSCDC), which computes SE relationships in both the latent and input spaces. This dual-focus approach captures multi-source SE relationships, enhancing the quality of the SE matrix. Additionally, we designed a convolutional fusion module that effectively integrates the multiple SE matrices through a learnable fusion approach. Experimental results across various datasets validate the superiority of our DSCDC compared to competing methods. Ablation studies further confirm the effectiveness of the proposed modules.







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Acknowledgements
This work was supported by Natural Science Basic Research Program of Shaanxi (2024JC-YBMS-473), Key Scientific Research Program of Education Department of Shaanxi Provincial Government (22JS019), and Xi’an Major Scientific and Technological Achievements Transformation Industrialization Project (23CGZHCYH0008).
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ML and BY wrote the main manuscript text. TX and SH are respectively responsible for reviewing and drawing. All authors reviewed the manuscript.
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Li, M., Yang, B., Xue, T. et al. Deep subspace clustering using dual self-expressiveness and convolutional fusion. J Supercomput 81, 390 (2025). https://doi.org/10.1007/s11227-024-06885-1
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DOI: https://doi.org/10.1007/s11227-024-06885-1