A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing. Our approach is in the same spirit as a Lagrangian decomposition approach in that the row and column constraints are satisfied separately with a Lagrange multiplier used to equate the two "solutions". Due to the unavoidable symmetries in graph isomorphism (resulting in multiple global minima), we add a symmetry-breaking self-amplification term in order to obtain a permutation matrix. With the application of a fixpoint preserving algebraic transformation to both the distance measure and self-amplification terms, we obtain a Lagrangian relaxation network. The network performs minimization with respect to the Lagrange parameters and maximization with respect to the permutation matrix variables. Simulation results are shown on 100 node random graphs and for a wide range of connectivities.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/72.548165 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.
Entropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFAn effective encryption approach using a combination of a non-chain ring and a four-dimensional chaotic map.
Cogn Neurodyn
December 2025
Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan.
Algebraic structures are highly effective in designing symmetric key cryptosystems; however, if the key space is not sufficiently large, such systems become vulnerable to brute-force attacks. To address this challenge, our research focuses on enlarging the key space in symmetric key schemes by integrating the non-chain ring with a four-dimensional chaotic system. While chaotic maps offer significant potential for data processing, relying solely on them does not fully leverage their operational advantages.
View Article and Find Full Text PDFA Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM.
Entropy (Basel)
November 2024
Faculty of Information Engineering, Quzhou College of Technology, Quzhou 324000, China.
Rolling bearings, as critical components of rotating machinery, significantly influence equipment reliability and operational efficiency. Accurate fault diagnosis is therefore crucial for maintaining industrial production safety and continuity. This paper presents a new fault diagnosis method based on FCEEMD multi-complexity low-dimensional features and directed acyclic graph LSTSVM.
View Article and Find Full Text PDFDiagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.
Eur J Radiol
December 2024
Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Austria.
Objectives: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.
Materials And Methods: In this single-center study, patients undergoing 3 T MRI who also had histology examination were retrospectively enrolled. SNAPSHOT-FLASH sequence for T1 mapping, radial turbo-spin-echo sequence for T2 mapping and spin-echo echo-planar-imaging magnetic resonance elastography (MRE) sequences were analyzed.
Population-aware permutation-based significance thresholds for genome-wide association studies.
Bioinform Adv
October 2024
Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany.
Motivation: Permutation-based significance thresholds have been shown to be a robust alternative to classical Bonferroni significance thresholds in genome-wide association studies (GWAS) for skewed phenotype distributions. The recently published method permGWAS introduced a batch-wise approach to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!