# Sparse Representation Classification via Screening for Graphs

## TL;DR

This paper introduces a screening-based implementation of the sparse representation classifier (SRC) for graph data, proving its consistency under stochastic blockmodels and demonstrating comparable accuracy with improved speed.

## Contribution

The paper presents a novel screening-based SRC method for graphs, establishing its theoretical consistency and practical efficiency over existing approaches.

## Key findings

- The screening-based SRC is equivalent to the original under certain conditions.
- The new method achieves similar classification accuracy as traditional SRC.
- It significantly reduces computational time in experiments.

## Abstract

The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for random graphs drawn from stochastic blockmodels. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance but significantly faster.