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 ...

Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

Abstract: With the continuous improvement of power grid stability and reliability requirements, improving the efficiency and accuracy of power system simulation has become an important research topic.

This is a preview. Log in through your library . Abstract Matrix factorization in numerical linear algebra (NLA) typically serves the purpose of restating some given problem in such a way that it can ...

Abstract: This article analyzes the composition and characteristics of echo signals in a pseudorandom-coded ground-penetrating radar (GPR). Based on these characteristics, an innovative low-rank ...

An important problem in multivariate statistics is the estimation of covariance matrices. We consider a class of nonparametric covariance models in which the entries in the covariance matrix depend on ...

wNMFx implements a simple version of Non-Negative Matrix Factorization (NMF) that utilizes a weight matrix to weight the importance of each feature in each sample of the data matrix to be factorized.