# Accelerated Sparse Subspace Clustering

arXiv: https://arxiv.org/abs/1711.00126 · HTML: https://mlsanity.com/paper/1711.00126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications](https://mlsanity.com/paper/2107.10484/) (2021)

## Abstract (arXiv)

State-of-the-art algorithms for sparse subspace clustering perform spectral clustering on a similarity matrix typically obtained by representing each data point as a sparse combination of other points using either basis pursuit (BP) or orthogonal matching pursuit (OMP). BP-based methods are often prohibitive in practice while the performance of OMP-based schemes are unsatisfactory, especially in settings where data points are highly similar. In this paper, we propose a novel algorithm that exploits an accelerated variant of orthogonal least-squares to efficiently find the underlying subspaces. We show that under certain conditions the proposed algorithm returns a subspace-preserving solution. Simulation results illustrate that the proposed method compares favorably with BP-based method in terms of running time while being significantly more accurate than OMP-based schemes.