Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...

Tom Bowen is a senior editor who loves adventure games and RPGs. He's been playing video games for several decades now and writing about them professionally since 2020. Although he dabbles in news and ...

Wayne, Pete and Mira treat a critically ill baby. Eliza’s patient confronts death. Eliza visits a terminally ill patient requesting help with voluntary assisted dying, only to discover the woman’s ...

1 Institute for Theoretical Physics, University of Bremen, Bremen, Germany 2 Institute of Electrodynamics and Microelectronics (ITEM.ids), University of Bremen, Bremen, Germany Considering biological ...

ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...

ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the ...

Ghent University, Department of Solid State Sciences, Krijgslaan 281 S1, 9000 Ghent, Belgium ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

Abstract: Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) decompose non-negative high-dimensional data into non-negative low-rank components. NMF and NTF methods ...