Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder

Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from...