# Robust Subspace Clustering with Compressed Data

arXiv: https://arxiv.org/abs/1803.11305 · HTML: https://mlsanity.com/paper/1803.11305/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering](https://mlsanity.com/paper/2106.09874/) (2021)
- [Adaptive Attribute and Structure Subspace Clustering Network](https://mlsanity.com/paper/2109.13742/) (2021)
- [Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling](https://mlsanity.com/paper/2404.04403/) (2024)
- [Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency](https://mlsanity.com/paper/2508.01254/) (2025)
- [Low-rank Dictionary Learning for Unsupervised Feature Selection](https://mlsanity.com/paper/2106.11102/) (2021)
- [Late Fusion Multi-view Clustering via Global and Local Alignment Maximization](https://mlsanity.com/paper/2208.01198/) (2022)

## Abstract (arXiv)

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called compressive robust subspace clustering, which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.