Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification...

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Bibliographic Details
Main Authors: Dodero, Luca, Ha Quang Minh, San Biagio, Marco, Murino, Vittorio, Sona, Diego
Format: Conference Proceeding
Language:English
Subjects:
Autism
classification
Connectomics
Kernel
kernel methods
Laplace equations
Manifolds
Measurement
Riemannian manifold
Support vector machines
Symmetric matrices
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Summary:An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2015.7163812