Regression-based Hypergraph Learning for Image Clustering and Classification

2016-03-14 · Sheng Huang, Dan Yang, Bo Liu, Xiaohong Zhang

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.

📄 PDF Abstract BibTeX arXiv:1603.04150

Code (0)

등록된 구현이 없습니다.

Tasks

ClassificationClusteringGeneral Classification Image Clustering regression

Methods 이 논문이 사용한 방법론

Spectral Clustering Spectral clustering has attracted increasing attention due to the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the…

Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets

2026-06-08 · Fuli Wang, Wei Qian, Daniel L. Lau, Gonzalo R. Arce arxiv

Convolutions have successfully transitioned from image processing to the complex realm of non-Euclidean higher-order domains, particularly in hypergraphs. Despite the success in convolution, the exploration of a popular …

Anomaly Detection

Hypergraph Vision Transformers: Images are More than Nodes, More than Edges

2025-04-11 · CVPR 2025 1 · Joshua Fixelle

Recent advancements in computer vision have highlighted the scalability of Vision Transformers (ViTs) across various tasks, yet challenges remain in balancing adaptability, computational efficiency, and the ability to mo…

Clustering Computational Efficiency Diversity image-classification+3

Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification

2016-03-03 · Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu 외

Graph model is emerging as a very effective tool for learning the complex structures and relationships hidden in data. Generally, the critical purpose of graph-oriented learning algorithms is to construct an informative …

Clustering General Classification graph construction Image Clustering+1

On The Effect of Hyperedge Weights On Hypergraph Learning

2014-10-24 · Sheng Huang, Ahmed Elgammal, Dan Yang

Hypergraph is a powerful representation in several computer vision, machine learning and pattern recognition problems. In the last decade, many researchers have been keen to develop different hypergraph models. In contra…

Clustering Graph Learning

Context-Aware Hypergraph Construction for Robust Spectral Clustering

2014-01-04 · Xi Li, Weiming Hu, Chunhua Shen, Anthony Dick 외

Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we propose a context-aware hypergraph similarity measure (CAHSM), which leads to robust spectral clustering in the case of noisy data.…

Clustering hypergraph partitioning