2020/10/24 by Muhammad Yousefnezhad, Yousefnezhad, Muhammad, Alessandro Selvitella +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Functional Analysis (math.FA) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC) #cs.AI #cs.LG #eess.IV #electronic engineering #information engineering #math.FA #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2010.15594
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. The Supplementary Material: https://www.yousefnezhad.com/publications/NeurIPS2020_Paper4157_SuppMat.zip
arxiv created 2020/10/24 · arxiv updated 2020/10/30
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site. SSTL first extracts a set of common features for all subjects in each site. It then uses TL to map these site-specific features to a site-independent shared space in order to improve the performance of the MVPA. SSTL uses a scalable optimization procedure that works effectively for high-dimensional fMRI datasets. The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space. We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.