Side Information Dependence as a Regularizer for Analyzing Human Brain Conditions across Cognitive Experiments

Shuo Zhou, Wenwen Li, Christopher Cox, and Haiping Lu

Abstract:

The increasing of public neuroimaging datasets opens a door to analyzing homogeneous human brain conditions across
datasets by transfer learning (TL). However, neuroimaging data are high-dimensional, noisy, and with small sample
sizes. It is challenging to learn a robust model for data across different cognitive experiments and subjects. A recent TL
approach minimizes domain dependence to learn common cross-domain features, via the Hilbert-Schmidt Independence
Criterion (HSIC). Inspired by this approach and the multisource TL theory, we propose a Side Information Dependence
Regularization (SIDeR) learning framework for TL in brain condition decoding. Specifically, SIDeR simultaneously minimizes the empirical risk and the statistical dependence on the domain side information, to reduce the theoretical generalization error bound. We construct 17 brain decoding TL tasks using public neuroimaging data for evaluation. Comprehensive experiments validate the superiority of SIDeR over ten competing methods, particularly an average improvement of 15.6% on the TL tasks with multi-source experiments