mlsanityData to 7 Oct 2026

Adaptive Graph Convolutional Subspace Clustering

arXiv 2305.03414 ↗First posted 5 May 2023cs.LG cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC outperforms some related methods as well as some deep models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

18 of 49 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2019Simplifying Graph Convolutional Networkscited by 1,470
  4. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  5. 2017Deep Subspace Clustering Networkscited by 94
  6. 2019Deep Comprehensive Correlation Mining for Image Clusteringcited by 80
  7. 2018Deep k-Means: Jointly clustering with k-Means and learning representationscited by 50
  8. 2014Robust and Efficient Subspace Segmentation via Least Squares Regressioncited by 47
  9. 2018Subspace Clustering by Block Diagonal Representationcited by 38
  10. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  11. 2020A Critique of Self-Expressive Deep Subspace Clusteringcited by 18
  12. 2020Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Imagecited by 16
6 more
  1. 2020Doubly Stochastic Subspace Clusteringcited by 14
  2. 2021Towards Clustering-friendly Representations: Subspace Clustering via Graph Filteringcited by 14
  3. 2016Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifoldscited by 10
  4. 2020Maximum Entropy Subspace Clustering Networkcited by 9
  5. 2021Adaptive Attribute and Structure Subspace Clustering Networkcited by 9
  6. 2020Learnable Subspace Clusteringcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.

4 papers.

  1. 2025Exploring a Principled Framework for Deep Subspace Clusteringcited by 7
  2. 2023One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clusteringcited by 0
  3. 2026Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learningcited by 0
  4. 2026Learning Deep Modality-Shared Self-Expressiveness for Image Clustering with Textual Informationcited by 0