GitHub

Matrix Decomposition Algorithms

Welcome to the nlp-2.1-matrix-decomposition repository! This project provides a collection of algorithms for matrix decomposition, a fundamental concept in linear algebra. Whether you're working on ...
IEEE

Robust Non-Negative Matrix Tri-Factorization with Dual Hyper-Graph Regularization

Abstract: Non-negative Matrix Factorization (NMF) has been an ideal tool for machine learning. Non-negative Matrix Tri-Factorization (NMTF) is a generalization of NMF that incorporates a third ...
IEEE

3-D Random Noise Attenuation Using Stable CUR Matrix Decomposition

Abstract: The low-rank property of seismic data has been successfully used for attenuating seismic random noise using a rank-reduction processing; however, traditional rank-reduction methods based on ...
Frontiers

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis

Lupus nephritis (LN) is a complex autoimmune disease affecting the kidneys and represents a significant health concern due to its diverse clinical manifestations and heterogeneous nature (1). Progress ...
C&EN

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...
Ars Technica

Matrix multiplication advancement could lead to faster, more efficient AI models

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...
Visual Studio Magazine

Matrix Inverse from Scratch Using SVD Decomposition with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...