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Deep Subspace Clustering Networks

arXiv 1709.02508 ↗First posted 8 Sep 2017cs.CVcited by 94

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Abstract (arXiv)

We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2017.

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Citing papers per year: 2021: 22; 2022: 20; 2023: 16; 2024: 11; 2025: 16; 2026: 9.

94 papers.

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  1. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  2. 2022Deep Clustering: A Comprehensive Surveycited by 78
  3. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  4. 2022Neural Manifold Clustering and Embeddingcited by 44
  5. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
  6. 2021Explaining Latent Representations with a Corpus of Examplescited by 30
  7. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  8. 2022Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategycited by 29
  9. 2026Do Sparse Autoencoders Capture Concept Manifolds?cited by 25
  10. 2021Learning a Self-Expressive Network for Subspace Clusteringcited by 24
  11. 2023Spatiotemporal Self-supervised Learning for Point Clouds in the Wildcited by 23
  12. 2022User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systemscited by 20
38 more
  1. 2023DivClust: Controlling Diversity in Deep Clusteringcited by 18
  2. 2022Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clusteringcited by 15
  3. 2023End-to-end Differentiable Clustering with Associative Memoriescited by 15
  4. 2021Towards Clustering-friendly Representations: Subspace Clustering via Graph Filteringcited by 14
  5. 2022Strongly Augmented Contrastive Clusteringcited by 14
  6. 2023Deep Multi-View Subspace Clustering with Anchor Graphcited by 13
  7. 2023Unsupervised Manifold Linearizing and Clusteringcited by 12
  8. 2021CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clusteringcited by 11
  9. 2021Deep Clustering by Semantic Contrastive Learningcited by 11
  10. 2023Graph Convolution Based Efficient Re-Ranking for Visual Retrievalcited by 11
  11. 2021A Simple Approach to Automated Spectral Clusteringcited by 10
  12. 2025A Comprehensive Survey on Spectral Clustering with Graph Structure Learningcited by 10
  13. 2021Adaptive Attribute and Structure Subspace Clustering Networkcited by 9
  14. 2022Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspectivecited by 8
  15. 2022Consistency and Diversity induced Human Motion Segmentationcited by 7
  16. 2025Exploring a Principled Framework for Deep Subspace Clusteringcited by 7
  17. 2021Large-Scale Hyperspectral Image Clustering Using Contrastive Learningcited by 6
  18. 2021Top-Down Deep Clustering with Multi-generator GANscited by 6
  19. 2023Online Unsupervised Video Object Segmentation via Contrastive Motion Clusteringcited by 6
  20. 2024Spectral Subspace Clustering for Attributed Graphscited by 6
  21. 2022NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clusteringcited by 5
  22. 2024Nonlinear subspace clustering by functional link neural networkscited by 5
  23. 2023Rotation and Translation Invariant Representation Learning with Implicit Neural Representationscited by 4
  24. 2023Adaptive Graph Convolutional Subspace Clusteringcited by 4
  25. 2021Smoothed Multi-View Subspace Clusteringcited by 3
  26. 2022Unsupervised Deep Discriminant Analysis Based Clusteringcited by 3
  27. 2022Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspectivecited by 3
  28. 2023De-coupling and De-positioning Dense Self-supervised Learningcited by 3
  29. 2023Deep Double Self-Expressive Subspace Clusteringcited by 3
  30. 2026Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifoldscited by 3
  31. 2022On the Robustness of Deep Clustering Models: Adversarial Attacks and Defensescited by 2
  32. 2025Bootstrap Deep Spectral Clustering with Optimal Transportcited by 2
  33. 2021Provable Data Clustering via Innovation Searchcited by 1
  34. 2021Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clusteringcited by 1
  35. 2021PMSSC: Parallelizable multi-subset based self-expressive model for subspace clusteringcited by 1
  36. 2021Attentive Multi-View Deep Subspace Clustering Netcited by 1
  37. 2022Fine-grained Graph Learning for Multi-view Subspace Clusteringcited by 1
  38. 2023Deep Structure and Attention Aware Subspace Clusteringcited by 1

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