Structure-Preserving Graph Kernel for Brain Network Classification
- URL: http://arxiv.org/abs/2111.10803v1
- Date: Sun, 21 Nov 2021 12:03:19 GMT
- Title: Structure-Preserving Graph Kernel for Brain Network Classification
- Authors: Zhaomin Kong, Aditya Kendre, Jun Yu, Hao Peng, Carl Yang, Lichao Sun,
Alex Leow and Lifang He
- Abstract summary: We show how to leverage the naturally available structure within the graph representation to encode prior knowledge in the kernel.
The proposed approach has the advantage of being clinically interpretable.
- Score: 38.707747282886935
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a novel graph-based kernel learning approach for
connectome analysis. Specifically, we demonstrate how to leverage the naturally
available structure within the graph representation to encode prior knowledge
in the kernel. We first proposed a matrix factorization to directly extract
structural features from natural symmetric graph representations of connectome
data. We then used them to derive a structure-persevering graph kernel to be
fed into the support vector machine. The proposed approach has the advantage of
being clinically interpretable. Quantitative evaluations on challenging HIV
disease classification (DTI- and fMRI-derived connectome data) and emotion
recognition (EEG-derived connectome data) tasks demonstrate the superior
performance of our proposed methods against the state-of-the-art. Results
showed that relevant EEG-connectome information is primarily encoded in the
alpha band during the emotion regulation task.
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