Scalable Deep Subspace Clustering Network

• URL: http://arxiv.org/abs/2512.21434v1
• Date: Wed, 24 Dec 2025 21:46:38 GMT
• Title: Scalable Deep Subspace Clustering Network
• Abstract summary: We propose SDSNet, a deep subspace clustering framework that achieves $mathcalO(n)$ complexity through landmark-based approximation.<n>We show that SDSNet achieves comparable clustering quality to state-of-the-art methods with significantly improved computational efficiency.
• Score: 6.321283533425182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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