Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability

Autor:	Patsolic, Heather, Adali, Sancar, Vogelstein, Joshua T., Park, Youngser, Friebe, Carey E., Li, Gongkai, Lyzinski, Vince
Rok vydání:	2014
Předmět:	Statistics - Machine Learning Statistics - Applications Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated data examples, we demonstrate the versatility of our algorithm in matching graphs with various characteristics--weightedness, directedness, loopiness, many-to-one and many-to-many matchings, and soft seedings. Comment: 26 pages, 7 figures. Updated content and added application of simultaneous matching for several time-steps for zebrafish connectomes
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1401.3813 Zobrazit plný text záznamu View this record from Arxiv