# Sketched Subspace Clustering

arXiv: https://arxiv.org/abs/1707.07196 · HTML: https://mlsanity.com/paper/1707.07196/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2017.

## Built on by (most cited first, 11 of 11)

- [Learning a Self-Expressive Network for Subspace Clustering](https://mlsanity.com/paper/2110.04318/) (2021)
- [Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering](https://mlsanity.com/paper/2206.05553/) (2022)
- [Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection](https://mlsanity.com/paper/2210.04271/) (2022)
- [Detecting adversaries in Crowdsourcing](https://mlsanity.com/paper/2110.04117/) (2021)
- [Multilayer Graph Approach to Deep Subspace Clustering](https://mlsanity.com/paper/2401.17033/) (2024)
- [Contrastive Learning Subspace for Text Clustering](https://mlsanity.com/paper/2408.14119/) (2024)
- [Robust Kernel Sparse Subspace Clustering](https://mlsanity.com/paper/2401.17035/) (2024)
- [Subspace Clustering in Wavelet Packets Domain](https://mlsanity.com/paper/2406.03819/) (2024)
- [Interpretable label-free self-guided subspace clustering](https://mlsanity.com/paper/2411.17291/) (2024)
- [Label-independent hyperparameter-free self-supervised single-view deep subspace clustering](https://mlsanity.com/paper/2504.18179/) (2025)
- [High Rank Matrix Completion via Grassmannian Proxy Fusion](https://mlsanity.com/paper/2602.02565/) (2026)

## Abstract (arXiv)

The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of their high clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data. Sketch-SC accelerates the computationally heavy parts of state-of-the-art SC approaches by compressing the data matrix across both dimensions using random projections, thus enabling fast and accurate large-scale SC. Performance analysis as well as extensive numerical tests on real data corroborate the potential of Sketch-SC and its competitive performance relative to state-of-the-art scalable SC approaches.