Scalable Deep Subspace Clustering Network
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908731724267520 |
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| author | Mrabah, Nairouz Bouguessa, Mohamed Sami, Sihem |
| author_facet | Mrabah, Nairouz Bouguessa, Mohamed Sami, Sihem |
| contents | Subspace clustering methods face inherent scalability limits due to the $O(n^3)$ cost (with $n$ denoting the number of data samples) of constructing full $n\times n$ affinities and performing spectral decomposition. While deep learning-based approaches improve feature extraction, they maintain this computational bottleneck through exhaustive pairwise similarity computations. We propose SDSNet (Scalable Deep Subspace Network), a deep subspace clustering framework that achieves $\mathcal{O}(n)$ complexity through (1) landmark-based approximation, avoiding full affinity matrices, (2) joint optimization of auto-encoder reconstruction with self-expression objectives, and (3) direct spectral clustering on factorized representations. The framework combines convolutional auto-encoders with subspace-preserving constraints. Experimental results demonstrate that SDSNet achieves comparable clustering quality to state-of-the-art methods with significantly improved computational efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_21434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Deep Subspace Clustering Network Mrabah, Nairouz Bouguessa, Mohamed Sami, Sihem Computer Vision and Pattern Recognition Machine Learning I.2.6; I.5.3 Subspace clustering methods face inherent scalability limits due to the $O(n^3)$ cost (with $n$ denoting the number of data samples) of constructing full $n\times n$ affinities and performing spectral decomposition. While deep learning-based approaches improve feature extraction, they maintain this computational bottleneck through exhaustive pairwise similarity computations. We propose SDSNet (Scalable Deep Subspace Network), a deep subspace clustering framework that achieves $\mathcal{O}(n)$ complexity through (1) landmark-based approximation, avoiding full affinity matrices, (2) joint optimization of auto-encoder reconstruction with self-expression objectives, and (3) direct spectral clustering on factorized representations. The framework combines convolutional auto-encoders with subspace-preserving constraints. Experimental results demonstrate that SDSNet achieves comparable clustering quality to state-of-the-art methods with significantly improved computational efficiency. |
| title | Scalable Deep Subspace Clustering Network |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.6; I.5.3 |
| url | https://arxiv.org/abs/2512.21434 |