Alapfogalmak
Efficient hypergraph matching using CUR decomposition.
Kivonat
The article introduces CURSOR, a novel framework for hypergraph matching that leverages CUR tensor decomposition. By utilizing a cascaded second and third-order approach, CURSOR significantly reduces time complexity and tensor density in large-scale graph matching. The method integrates seamlessly into existing algorithms, improving performance while lowering computational costs. Experimental results demonstrate the superiority of CURSOR over traditional methods on synthetic datasets and benchmark sets.
Statisztikák
Large-Scale Synthetic Dataset: 1000-vs-1000 matching problem with high accuracy.
House and Hotel Dataset: Achieved high accuracy in sequence-matching tasks.
Car and Motorbike Dataset: Improved matching performance with sparser tensors.
Idézetek
"To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources."
"A probability relaxation labeling (PRL)-based matching algorithm is developed specifically suitable for sparse tensors."
"The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods."