Accelerated preconditioned convex difference algorithm solving multilayer graph Ginzburg-Landau energy minimization for data classification

doi:10.1016/j.eswa.2025.131037

Accelerated preconditioned convex difference algorithm solving multilayer graph Ginzburg-Landau energy minimization for data classification

2026-01-05

0

PRE

AI

DOI：10.1016/j.eswa.2025.131037

Original

Original request for help

Save

Abstract

En 中文

• A new GL model for multilayer graphs effectively integrates multilayer complementary information to enhance boundary recognition. • MPDC integrates convex difference and preconditioning, reducing the number of parameters to tune, eliminating the need for eigenvalue decomposition, and improving both efficiency and accuracy. • AMPDC leverages NFFT to reduce computational and storage costs, efficiently handling large-scale data. • MPDC and AMPDC show high accuracy and efficiency across datasets, proving strong generalizability and utility.

Accelerated preconditioned convex difference algorithm solving multilayer graph Ginzburg-Landau energy minimization for data classification

Abstract

Journal

Organization