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18 papers · ranked by Valyu relevance

01
PPubMed2 Jul 2010
A flexible R package for nonnegative matrix factorization
Renaud Gaujoux, Cathal Seoighe
Background Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from…
97relevance
02
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…
96relevance
03
PPubMed25 Mar 2008
Theorems on Positive Data: On the Uniqueness of NMF
Hans Laurberg, Mads Græsbøll Christensen, Mark D. Plumbley, Lars Kai Hansen + 1 more
'Lars Kai Hansen' 'Søren Holdt Jensen'] We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and…
94relevance
04
PPubMed28 Sep 2021
Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
Pascal Fernsel, Fabiana Zama, Elena Loli Piccolomini
Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering methods. Motivated by…
94relevance
05
PPubMed6 Jul 2008
Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems
Rafal Zdunek, Andrzej Cichocki
Recently, a considerable growth of interest in projected gradient (PG) methods has been observed due to their high efficiency in solving large-scale convex minimization problems subject to linear constraints. Since the minimization problems underlying nonnegative matrix factorization (NMF) of large matrices well…
94relevance
06
PPubMed6 Jul 2008
Advances in Nonnegative Matrix and Tensor Factorization
A. Cichocki, M. Mørup, P. Smaragdis, W. Wang + 1 more
Nonnegative matrix factorization (NMF) and its extension known as nonnegative tensor factorization (NTF) are emerging techniques that have been proposed recently. The goal of NMF/NTF is to decompose a nonnegative data matrix into a product of lower-rank nonnegative matrices or tensors (i.e., multiway arrays). An NMF…
93relevance
07
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…
93relevance
08
PPubMed7 Oct 2010
Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)
Elina Tjioe, Michael W Berry, Ramin Homayouni
Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases…
92relevance
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…
92relevance
10
PPubMed16 Jan 2026
Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data
Ragnhild Laursen, Han Chen, Jack Demaray, Karin Pelka + 1 more
Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales…
91relevance
11
PPubMed20 Dec 2013
Discriminant Projective Non-Negative Matrix Factorization Discriminant Projective NMF
Naiyang Guan, Xiang Zhang, Zhigang Luo, Dacheng Tao + 2 more
'Xi-Nian Zuo'] Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers WT X as their coefficients, i.e., X≈WWT X. Since PNMF learns the natural parts-based representation Wof X, it has been…
90relevance
12
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
13
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
14
PPubMed21 Oct 2013
Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method
Naiyang Guan, Lei Wei, Zhigang Luo, Dacheng Tao + 1 more
Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix to the product of two lower-rank nonnegative factor matrices, i.e., and ( ) and aims to preserve the local geometric structure of the dataset by minimizing squared Euclidean distance or Kullback-Leibler (KL) divergence…
88relevance
15
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…
87relevance
16
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
17
PPubMed6 Nov 2024
Few-shot learning for inference in medical imaging with subspace feature representations Few-shot learning for inference in medical imaging with subspace feature representation
Jiahui Liu, Keqiang Fan, Xiaohao Cai, Mahesan Niranjan + 1 more
Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset…
85relevance
18
PPubMed18 Jan 2010
Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery
Henry Han
Background As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer…
69relevance

Results for Nonnegative Matrix Factorization

A flexible R package for nonnegative matrix factorization

Probabilistic Latent Variable Models as Nonnegative Factorizations

Theorems on Positive Data: On the Uniqueness of NMF

Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems

Advances in Nonnegative Matrix and Tensor Factorization

Block-Active ADMM to Minimize NMF with Bregman Divergences

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

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

Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data

Discriminant Projective Non-Negative Matrix Factorization Discriminant Projective NMF

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

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

Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method

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

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

Few-shot learning for inference in medical imaging with subspace feature representations Few-shot learning for inference in medical imaging with subspace feature representation

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

A flexible R package for nonnegative matrix factorization

Probabilistic Latent Variable Models as Nonnegative Factorizations

Theorems on Positive Data: On the Uniqueness of NMF

Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems

Advances in Nonnegative Matrix and Tensor Factorization

Block-Active ADMM to Minimize NMF with Bregman Divergences

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

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

Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data

Discriminant Projective Non-Negative Matrix Factorization Discriminant Projective NMF

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

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

Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method

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

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

Few-shot learning for inference in medical imaging with subspace feature representations Few-shot learning for inference in medical imaging with subspace feature representation

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery