Abstract Details
Activity Number:
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402
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #313430
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View Presentation
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Title:
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Seeded Graph Matching
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Author(s):
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Vince Lyzinski*+
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Companies:
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Johns Hopkins University
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Keywords:
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graph matching ;
pattern recognition ;
multidimensional scaling ;
machine learning ;
computing methodologies
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Abstract:
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Given two graphs, the graph matching problem seeks to find a correspondence (i.e. "matching") between the vertex sets that best preserves structure across the graphs. In the seeded graph matching paradigm, we assume that we are further provided a partial correspondence between subsets of the vertex sets and leverage this information to find the correspondence for the remaining unseeded vertices. I'll present a robust algorithm for approximately solving the seeded graph matching problem, highlighting along the way applications of our methodology in connectomics and a natural extension of our methodology to vertex classification. This is joint work with Sancar Adali, Youngser Park, Carey Priebe and Joshua Vogelstein.
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Authors who are presenting talks have a * after their name.
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