Hypergraph-based convex semi-supervised unconstraint symmetric matrix factorization for image clustering

Wenjun Luo; Zezhong Wu; Nan Zhou

doi:10.1016/j.ins.2024.121138

ScienceGate Book Chapters

JOURNAL ARTICLE

Hypergraph-based convex semi-supervised unconstraint symmetric matrix factorization for image clustering

Wenjun Luo Zezhong Wu Nan Zhou

Year: 2024 Journal: Information Sciences Vol: 680 Pages: 121138-121138 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2024.121138

Get Full-Text PDF Get Analytical Report

Keywords:

Non-negative matrix factorization Cluster analysis Outlier Robustness (evolution) Hypergraph Computer science Matrix decomposition Mathematics Convex optimization Mathematical optimization Gradient descent Algorithm Pattern recognition (psychology) Artificial intelligence Regular polygon Eigenvalues and eigenvectors

Metrics

Cited By

1.59

FWCI (Field Weighted Citation Impact)

Refs

0.74

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Hypergraph-based convex semi-supervised unconstraint symmetric matrix factorization for image clustering

Metrics

Citation History

Topics

Related Documents

Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering

Multiview Clustering via Hypergraph Induced Semi-Supervised Symmetric Nonnegative Matrix Factorization

Adaptive hypergraph structure regularized semi-supervised non-negative matrix factorization for image clustering

Semi-supervised Clustering via Constrained Symmetric Non-negative Matrix Factorization

Node Importance-Based Semi-supervised Nonnegative Matrix Factorization for Image Clustering