M-M.E.S.S. provides low-rank solvers for large-scale symmetric matrix equations with sparse or sparse + low-rank coefficients. The main focus is on differential and algebraic Riccati equations ...

Researchers in mathematics have uncovered a new approach to matrix decomposition, which could pave the way for significant advances in areas such as signal and image processing, machine learning, and ...

Matrix decomposition is an area of linear algebra which is focused on expressing a matrix as a product of matrices with prescribed properties. (Photo credit: Merino et al., 2024) Imagine ...

Matrix decomposition is an area of linear algebra which is focused on expressing a matrix as a product of matrices with prescribed properties. (Photo credit: Merino et al., 2024) Imagine discovering ...

To address this challenge, we propose a missing data completion method based on group-sparse differential reweighted latent matrix factorization (GSDRMF). The proposed method mitigates the impact of ...

NMFk is a module of the SmartTensors ML framework (smarttensors.com). NMFk is a novel unsupervised machine learning methodology that allows for the automatic identification of the optimal number of ...

In this work, we introduce a sparse representation of fMRI data in the form of a ... For every volume a whole brain co-activation matrix is derived, and the sum of all co-activation matrices estimates ...