# Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

arXiv: https://arxiv.org/abs/2102.07943 · HTML: https://mlsanity.com/paper/2102.07943/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Subspace Clustering Networks](https://mlsanity.com/paper/1709.02508/) (2017)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit](https://mlsanity.com/paper/1507.01238/) (2015)
- [Robust and Efficient Subspace Segmentation via Least Squares Regression](https://mlsanity.com/paper/1404.6736/) (2014)
- [Robust Graph Learning from Noisy Data](https://mlsanity.com/paper/1812.06673/) (2018)
- [Self-Supervised Convolutional Subspace Clustering Network](https://mlsanity.com/paper/1905.00149/) (2019)
- [Multi-view Low-rank Sparse Subspace Clustering](https://mlsanity.com/paper/1708.08732/) (2017)
- [Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework](https://mlsanity.com/paper/1610.05211/) (2016)
- [Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering](https://mlsanity.com/paper/1608.05560/) (2016)
- [Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications](https://mlsanity.com/paper/1708.07850/) (2017)
- [Structured Graph Learning for Clustering and Semi-supervised Classification](https://mlsanity.com/paper/2008.13429/) (2020)
- [Twin Learning for Similarity and Clustering: A Unified Kernel Approach](https://mlsanity.com/paper/1705.00678/) (2017)
- [Structure Learning with Similarity Preserving](https://mlsanity.com/paper/1912.01197/) (2019)
- [Robust Subspace Clustering with Compressed Data](https://mlsanity.com/paper/1803.11305/) (2018)
- [Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces](https://mlsanity.com/paper/2006.04246/) (2020)
- [A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data](https://mlsanity.com/paper/1309.6487/) (2013)
- [Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs](https://mlsanity.com/paper/1802.07648/) (2018)
- [Multi-View Multiple Clustering](https://mlsanity.com/paper/1905.05053/) (2019)
- [Relation-Guided Representation Learning](https://mlsanity.com/paper/2007.05742/) (2020)
- [Latent Complete Row Space Recovery for Multi-view Subspace Clustering](https://mlsanity.com/paper/1912.07248/) (2019)
- [Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning](https://mlsanity.com/paper/1812.10012/) (2018)
- [Efficient Solvers for Sparse Subspace Clustering](https://mlsanity.com/paper/1804.06291/) (2018)
- [Latent Multi-view Semi-Supervised Classification](https://mlsanity.com/paper/1909.03712/) (2019)

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

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences](https://mlsanity.com/paper/2205.15075/) (2022)
- [Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering](https://mlsanity.com/paper/2204.12496/) (2022)
- [CDC: A Simple Framework for Complex Data Clustering](https://mlsanity.com/paper/2403.03670/) (2024)
- [A Simple Approach to Automated Spectral Clustering](https://mlsanity.com/paper/2107.12183/) (2021)
- [Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning](https://mlsanity.com/paper/2209.04187/) (2022)
- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [High-order Multi-view Clustering for Generic Data](https://mlsanity.com/paper/2209.10838/) (2022)
- [Exploring Correlations of Self-Supervised Tasks for Graphs](https://mlsanity.com/paper/2405.04245/) (2024)
- [Enhanced Latent Multi-view Subspace Clustering](https://mlsanity.com/paper/2312.14763/) (2023)
- [Efficient Multi-View Graph Clustering with Local and Global Structure Preservation](https://mlsanity.com/paper/2309.00024/) (2023)
- [Initialization for Nonnegative Matrix Factorization: a Comprehensive Review](https://mlsanity.com/paper/2109.03874/) (2021)
- [Scalable Multi-view Clustering with Graph Filtering](https://mlsanity.com/paper/2205.09228/) (2022)
- [Smoothed Multi-View Subspace Clustering](https://mlsanity.com/paper/2106.09875/) (2021)
- [Towards Learnable Anchor for Deep Multi-View Clustering](https://mlsanity.com/paper/2503.12427/) (2025)
- [Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models](https://mlsanity.com/paper/2305.02573/) (2023)
- [Spectral Clustering for Discrete Distributions](https://mlsanity.com/paper/2401.13913/) (2024)
- [Causally-Aware Unsupervised Feature Selection Learning](https://mlsanity.com/paper/2410.12224/) (2024)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [Graph Representation Learning via Aggregation Enhancement](https://mlsanity.com/paper/2201.12843/) (2022)
- [One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering](https://mlsanity.com/paper/2305.07386/) (2023)
- [Learning Structure Aware Deep Spectral Embedding](https://mlsanity.com/paper/2305.08215/) (2023)
- [Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation](https://mlsanity.com/paper/2509.15955/) (2025)
- [Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clustering](https://mlsanity.com/paper/2512.09810/) (2025)
- [Scalable Deep Subspace Clustering Network](https://mlsanity.com/paper/2512.21434/) (2025)
- [Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence](https://mlsanity.com/paper/2606.04857/) (2026)

## Abstract (arXiv)

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building a n× n graph, where n is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K-means clustering. Moreover, a model to process multi-view data is also proposed, which is linear scaled with respect to n. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.