# Deep Subspace Clustering Networks

arXiv: https://arxiv.org/abs/1709.02508 · HTML: https://mlsanity.com/paper/1709.02508/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 30 of 94)

- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Neural Manifold Clustering and Embedding](https://mlsanity.com/paper/2201.10000/) (2022)
- [Do Sparse Autoencoders Capture Concept Manifolds?](https://mlsanity.com/paper/2604.28119/) (2026)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Explaining Latent Representations with a Corpus of Examples](https://mlsanity.com/paper/2110.15355/) (2021)
- [Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy](https://mlsanity.com/paper/2203.17205/) (2022)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [Learning a Self-Expressive Network for Subspace Clustering](https://mlsanity.com/paper/2110.04318/) (2021)
- [Spatiotemporal Self-supervised Learning for Point Clouds in the Wild](https://mlsanity.com/paper/2303.16235/) (2023)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [DivClust: Controlling Diversity in Deep Clustering](https://mlsanity.com/paper/2304.01042/) (2023)
- [Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering](https://mlsanity.com/paper/2204.12496/) (2022)
- [End-to-end Differentiable Clustering with Associative Memories](https://mlsanity.com/paper/2306.03209/) (2023)
- [Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering](https://mlsanity.com/paper/2106.09874/) (2021)
- [Strongly Augmented Contrastive Clustering](https://mlsanity.com/paper/2206.00380/) (2022)
- [Deep Multi-View Subspace Clustering with Anchor Graph](https://mlsanity.com/paper/2305.06939/) (2023)
- [CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering](https://mlsanity.com/paper/2101.06883/) (2021)
- [Unsupervised Manifold Linearizing and Clustering](https://mlsanity.com/paper/2301.01805/) (2023)
- [Deep Clustering by Semantic Contrastive Learning](https://mlsanity.com/paper/2103.02662/) (2021)
- [Graph Convolution Based Efficient Re-Ranking for Visual Retrieval](https://mlsanity.com/paper/2306.08792/) (2023)
- [A Simple Approach to Automated Spectral Clustering](https://mlsanity.com/paper/2107.12183/) (2021)
- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [Adaptive Attribute and Structure Subspace Clustering Network](https://mlsanity.com/paper/2109.13742/) (2021)
- [Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective](https://mlsanity.com/paper/2204.04730/) (2022)
- [Consistency and Diversity induced Human Motion Segmentation](https://mlsanity.com/paper/2202.04861/) (2022)
- [Exploring a Principled Framework for Deep Subspace Clustering](https://mlsanity.com/paper/2503.17288/) (2025)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)
- [Top-Down Deep Clustering with Multi-generator GANs](https://mlsanity.com/paper/2112.03398/) (2021)

## 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.