Search · four archives

Results for non-negative matrix factorization

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

23 papers · ranked by Valyu relevance

01
PPubMed11 May 2008
Probabilistic Latent Variable Models as Nonnegative Factorizations
Madhusudana Shashanka, Bhiksha Raj, Paris Smaragdis
This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order…
95relevance
02
AarXiv28 Jul 2014
Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization.
Amy N. Langville, Carl D. Meyer, Russell Albright, James A. Cox + 1 more
'David Duling'] It is well-known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms [56]. Many NMF algorithms are sensitive with respect to the initialization of W or H or both. This is especially true of algorithms of the alternating…
95relevance
03
AarXiv22 Jul 2026
Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}
Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris
Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems. As the popularity of NMF has…
93relevance
04
BbioRxiv1 Sep 2021
Fast and interpretable non-negative matrix factorization for atlas-scale single cell data
Zachary J. DeBruine, J. Andrew Pospisilik, Timothy J. Triche
Non-negative matrix factorization (NMF) is a popular method for analyzing strictly positive data due to its relatively straightforward interpretation. However, NMF has a reputation as a less efficient alternative to the singular value decomposition (SVD), a standard operation that is highly optimized in most linear…
92relevance
05
BbioRxiv15 May 2018
Fast Nonnegative Matrix Factorization and Applications to Pattern Extraction, Deconvolution and Imputation
Xihui Lin, Paul C. Boutros
Nonnegative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence, and cannot handle missing values. In addition…
92relevance
06
PPubMed17 Aug 2023
Block-Active ADMM to Minimize NMF with Bregman Divergences
Xinyao Li, Akhilesh Tyagi, Loris Nanni
Over the last ten years, there has been a significant interest in employing nonnegative matrix factorization (NMF) to reduce dimensionality to enable a more efficient clustering analysis in machine learning. This technique has been applied in various image processing applications within the fields of computer vision…
92relevance
07
BbioRxiv12 Apr 2024
Non-negative matrix factorization and deconvolution as dual simplex problem
Denis Kleverov, Ekaterina Aladyeva, Alexey Serdyukov, Maxim N. Artyomov
Non-negative matrix factorization (NMF) is one of the most powerful linear algebra tools, which has found application in various areas of data analysis, including computational biology. Despite numerous optimization methods devised for NMF, our comprehension of the inherent topological structure within factorizable…
91relevance
08
AarXiv31 Oct 2019
Solving NMF with smoothness and sparsity constraints using PALM
Raimón Fabregat, Nelly Pustelnik, Paulo Gonçalvès, Pierre Borgnat
Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1999 by Lee and Seung , including in compression of data , document clustering , processing of audio spectrograms and astronomy. In this work…
91relevance
09
PPubMed30 Jul 2026
Efficient hybrid algorithm for nonnegative matrix factorization based on modified nonmonotone linear search NMF
Jing Wu, Wenbo Li, Lijun Su, Huiru Wang + 2 more
In this paper, we present a modified nonmonotone line search algorithm that employs a variable parameter to control the degree of nonmonotonicity. This modification enhances both the probability of identifying the global minimum and the rate of convergence. Within the framework of alternating nonnegative least squares…
91relevance
10
PPubMed31 Aug 2016
A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis
Stéphane Chrétien, Christophe Guyeux, Bastien Conesa, Régis Delage-Mouroux + 3 more
'Régis Delage-Mouroux' 'Michèle Jouvenot' 'Philippe Huetz' 'Françoise Descôtes'] Background Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing…
89relevance
11
PPubMed18 May 2016
Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health
Paul Fogel, Yann Gaston-Mathé, Douglas Hawkins, Fajwel Fogel + 3 more
'George Luta' 'S. Stanley Young' 'Igor Burstyn'] Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then…
89relevance
12
AarXiv2020
A Block Coordinate Descent-based Projected Gradient Algorithm for Orthogonal Non-negative Matrix Factorization
Soodabeh Asadi, Janez Povh
This article utilizes the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalise the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain…
89relevance
13
PPubMed12 Nov 2019
Application of machine learning techniques to electron microscopic/spectroscopic image data analysis
Shunsuke Muto, Motoki Shiga
The combination of scanning transmission electron microscopy (STEM) with analytical instruments has become one of the most indispensable analytical tools in materials science. A set of microscopic image/spectral intensities collected from many sampling points in a region of interest, in which multiple physical/chemical…
88relevance
14
BbioRxiv11 Jul 2022
Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS
Jeanette Johnson, Ashley Tsang, Jacob T. Mitchell, Emily Davis-Marcisak + 12 more
Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to…
88relevance
15
PPubMed11 Jun 2019
Fast optimization of non-negative matrix tri-factorization Fast optimization of non-negative matrix tri-factorization
Andrej Čopar, Blaž Zupan, Marinka Zitnik, Holger Fröhlich
Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses multiplicative update rules. This procedure has had limited success, and…
87relevance
16
AarXiv2014
Learning manifold to regularize nonnegative matrix factorization
Jim Jing-Yan Wang, Xin Gao
In this chapter we discuss how to learn an optimal manifold presentation to regularize nonegative matrix factorization (NMF) for data representation problems. NMF, which tries to represent a nonnegative data matrix as a product of two low rank nonnegative matrices, has been a popular method for data representation due…
87relevance
17
AarXiv2019
Nonnegative Low Rank Matrix Approximation for Nonnegative Matrices
Guang‐Jing Song, Michael K. Ng
This paper describes a new algorithm for computing Nonnegative Low Rank Matrix (NLRM) approximation for nonnegative matrices. Our approach is completely different from classical nonnegative matrix factorization (NMF) which has been studied for more than twenty five years. For a given nonnegative matrix, the usual NMF…
86relevance
18
AarXiv9 Jan 2026
A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
Eric Weine, Peter Carbonetto, Rafael A. Irizarry, Matthew Stephens
Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist, existing methods assume that the "parts" in the decomposition combine additively. This assumption may be natural in some settings, but not…
86relevance
19
BbioRxiv15 Apr 2022
The Decision of the Optimal Rank of a Non-negative Matrix Factorization Model for Gene Expression Datasets Utilizing Unit Invariant Knee Method
Emine Güven
There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. Recent utilization of non-negative matrix factorization (NMF) in computational biology has served its capability to derive essential details from a high amount of data in particular gene…
83relevance
20
BbioRxiv22 Jan 2022
Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data
Ragunathan Mariappan, Aishwarya Jayagopal, Ho Zong Sien, Vaibhav Rajan
In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative…
70relevance
21
CchemRxiv5 Jun 2023
Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…
63relevance
22
CchemRxiv7 Apr 2021
A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials
Arni Sturluson, Ali Raza, Grant D. McConachie, Daniel Siderius + 2 more
Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A…
62relevance
23
CchemRxiv21 Oct 2024
ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
52relevance

Results for non-negative matrix factorization

Probabilistic Latent Variable Models as Nonnegative Factorizations

Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization.

Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}

Fast and interpretable non-negative matrix factorization for atlas-scale single cell data

Fast Nonnegative Matrix Factorization and Applications to Pattern Extraction, Deconvolution and Imputation

Block-Active ADMM to Minimize NMF with Bregman Divergences

Non-negative matrix factorization and deconvolution as dual simplex problem

Solving NMF with smoothness and sparsity constraints using PALM

Efficient hybrid algorithm for nonnegative matrix factorization based on modified nonmonotone linear search NMF

A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

A Block Coordinate Descent-based Projected Gradient Algorithm for Orthogonal Non-negative Matrix Factorization

Application of machine learning techniques to electron microscopic/spectroscopic image data analysis

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Fast optimization of non-negative matrix tri-factorization Fast optimization of non-negative matrix tri-factorization

Learning manifold to regularize nonnegative matrix factorization

Nonnegative Low Rank Matrix Approximation for Nonnegative Matrices

A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link

The Decision of the Optimal Rank of a Non-negative Matrix Factorization Model for Gene Expression Datasets Utilizing Unit Invariant Knee Method

Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data

Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

Probabilistic Latent Variable Models as Nonnegative Factorizations

Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization.

Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}

Fast and interpretable non-negative matrix factorization for atlas-scale single cell data

Fast Nonnegative Matrix Factorization and Applications to Pattern Extraction, Deconvolution and Imputation

Block-Active ADMM to Minimize NMF with Bregman Divergences

Non-negative matrix factorization and deconvolution as dual simplex problem

Solving NMF with smoothness and sparsity constraints using PALM

Efficient hybrid algorithm for nonnegative matrix factorization based on modified nonmonotone linear search NMF

A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

A Block Coordinate Descent-based Projected Gradient Algorithm for Orthogonal Non-negative Matrix Factorization

Application of machine learning techniques to electron microscopic/spectroscopic image data analysis

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Fast optimization of non-negative matrix tri-factorization Fast optimization of non-negative matrix tri-factorization

Learning manifold to regularize nonnegative matrix factorization

Nonnegative Low Rank Matrix Approximation for Nonnegative Matrices

A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link

The Decision of the Optimal Rank of a Non-negative Matrix Factorization Model for Gene Expression Datasets Utilizing Unit Invariant Knee Method

Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data

Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning