CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition

arXiv:2402.16594v2 Announce Type: replace
Abstract: To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods, despite the sparsity of their compatibility tensor, still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match, and then the core of …

abstract accuracy algorithms ann approximate nearest neighbor arxiv computational cs.cv cursor graph hypergraph mixed novel resources scalable scale sparsity tensor tree type work