Regression-based Hypergraph Learning for Image Clustering and Classification

TL;DR

This paper introduces a novel regression-based hypergraph model that improves image clustering and classification by integrating regression techniques into hypergraph learning frameworks, demonstrating superior performance on multiple datasets.

Contribution

The paper proposes the Regression-based Hypergraph (RH) model and integrates it into spectral clustering and transduction frameworks, combining hypergraph and regression advantages for image analysis.

Findings

RH algorithms outperform existing methods on six image datasets.

RH inherits properties from hypergraph and regression models, enhancing clustering and classification.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regression-based Hypergraph (RH) which utilizes the regression models to construct the high quality hypergraphs. Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues. Sparse Representation and Collaborative Representation are employed to instantiate two RH instances and their RHSC and RHT algorithms. The experimental results on six popular image databases demonstrate that the proposed RH learning algorithms achieve promising image clustering and classification performances, and also validate that RH can inherit the desirable properties from both hypergraph models and regression models.