Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

Abstract

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification.

Solving the problem of simultaneous diagonalization of complex symmetric matrices via congruence

“…Consequently, it has applications in medical imaging analysis, see [1,4,17,24] and references therein. There are also connections with connectomes, namely weighted graphs were each node represents a certain part of the brain and each edge characterises the structural connection between the regions of a brain [14].…”