Taming dimensionality in big neuroimaging data: Efficient orthonormal projective NMF via stochastic learning and data compression
Abstract Large-scale neuroimaging datasets present remarkable opportunities for advancing our understanding of human brain structure and function. Data-driven pattern analysis methods such as Orthonormal Projective Non-negative Matrix Factorization (opNMF) are particularly well suited to uncover multivariate relationsh...