Semi-supervised tensor factorization for brain network analysis

Abstract

Brain networks characterize the temporal and/or spectral connections between brain regions and are inherently represented by multi-way arrays (tensors). In order to discover the underlying factors driving such connections, we need to derive compact representations from brain network data. Such representations should be discriminative so as to facilitate the identification of subjects performing different cognitive tasks or with different neurological disorders. In this paper, we propose semiBAT, a novel semi-supervised Brain network Analysis approach based on constrained Tensor factorization. semiBAT (1) leverages unlabeled resting-state brain networks for task recognition, (2) explores the temporal dimension to capture the progress, (3) incorporates classifier learning procedure to introduce supervision from labeled data, and (4) selects discriminative latent factors for different tasks. The Alternating Direction Method of Multipliers (ADMM) framework is utilized to solve the optimization objective. Experimental results on EEG brain networks illustrate the superior performance of the proposed semiBAT model on graph classification with a significant improvement 31.60% over plain vanilla tensor factorization. Moreover, the data-driven factors can be readily visualized which should be informative for investigating cognitive mechanisms. The software related to this paper is available at https://www.cs.uic.edu/∼bcao1/code/semibat.zip.