# Restricted Connection Orthogonal Matching Pursuit For Sparse Subspace Clustering

## TL;DR

The paper introduces RCOMP-SSC, a noise-robust, computationally efficient clustering algorithm that improves accuracy by restricting data point connections during Orthogonal Matching Pursuit, validated on synthetic and real datasets.

## Contribution

It proposes a novel RCOMP-SSC algorithm that enhances clustering accuracy and efficiency by restricting connections, and introduces a scalable control matrix framework for data point selection strategies.

## Key findings

- Outperforms existing methods in accuracy on synthetic and real data
- Reduces computational time compared to traditional SSC
- Demonstrates robustness to noise in clustering tasks

## Abstract

Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time.