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Shuffled Graph Classification: Theory and Connectome Applications

Shuffled Graph Classification: Theory and Connectome Applications We develop a formalism to address statistical pattern recognition of graph valued data. Of particular interest is the case of all graphs having the same number of uniquely labeled vertices. When the vertex labels are latent, such graphs are called shuffled graphs. Our formalism provides insight to trivially answer a number of open statistical questions including: (i) under what conditions does shuffling the vertices degrade classification performance and (ii) do universally consistent graph classifiers exist? The answers to these questions lead to practical heuristic algorithms with state-of-the-art finite sample performance, in agreement with our theoretical asymptotics. Applying these methods to classify sex and autism in two different human connectome classification tasks yields successful classification results in both applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Shuffled Graph Classification: Theory and Connectome Applications

Vogelstein, Joshua; Priebe, Carey
Journal of Classification , Volume 32 (1) – Mar 11, 2015
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References (22)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal, Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-015-9170-6
Publisher site
See Article on Publisher Site

Abstract

We develop a formalism to address statistical pattern recognition of graph valued data. Of particular interest is the case of all graphs having the same number of uniquely labeled vertices. When the vertex labels are latent, such graphs are called shuffled graphs. Our formalism provides insight to trivially answer a number of open statistical questions including: (i) under what conditions does shuffling the vertices degrade classification performance and (ii) do universally consistent graph classifiers exist? The answers to these questions lead to practical heuristic algorithms with state-of-the-art finite sample performance, in agreement with our theoretical asymptotics. Applying these methods to classify sex and autism in two different human connectome classification tasks yields successful classification results in both applications.

Journal

Journal of Classification – Springer Journals

Published: Mar 11, 2015

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