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23 papers · ranked by Valyu relevance

01
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…
91relevance
02
AarXiv2 May 2018
A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering
Dijana Tolić, Nino Antulov-Fantulin, Ivica Kopriva
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernelbased orthogonal…
89relevance
03
AarXiv16 Mar 2024
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning
Zhen Wang, Wenwen Min
—Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining. Graph Regularized Non-negative Matrix Factorization (GNMF) is an extension of NMF that incorporates graph regularization constraints. GNMF has demonstrated exceptional performance in clustering and…
86relevance
04
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…
85relevance
05
AarXiv2022
Non-Negative Matrix Factorization with Scale Data Structure Preservation
Rachid Hedjam, Abdelhamid Abdesselam, Abderrahmane Rahiche, Mohamed Cheriet
'Mohamed Cheriet'] The model described in this paper belongs to the family of non-negative matrix factorization methods designed for data representation and dimension reduction. In addition to preserving the data positivity property, it aims also to preserve the structure of data during matrix factorization. The idea…
82relevance
06
AarXiv2022
Supervised Class-pairwise NMF for Data Representation and Classification
Rachid Hedjam, Abdelhamid Abdesselam, Seyed Mohammad Jafar Jalali, Imran Khan + 1 more
'Imran Khan' 'Samir Brahim Belhaouari'] Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks, such as clustering, or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by…
79relevance
07
AarXiv2022
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Chong Peng, Yiqun Zhang, Yongyong Chen, Kang Zhao + 2 more
'Qiang Cheng'] Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse…
77relevance
08
BbioRxiv21 Oct 2022
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction
Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar
Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a…
75relevance
09
PPubMed5 Dec 2024
Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding
Mingming Li, Xingjie Wang, Chunhua Li, Anping Zeng + 1 more
Text embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose. However, the standard NMF approach, fundamentally based on the bag-of-words model, fails to utilize the contextual information of documents…
74relevance
10
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…
72relevance
11
AarXiv2022
Subspace Nonnegative Matrix Factorization for Feature Representation
Junhang Li, Jiao Wei, Can Tong, Tingting Shen + 5 more
'Shouliang Qi' 'Yudong Yao' 'Yueyang Teng'] Abstract—Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, whic h means treating all features equally. However, a subspace is often sufficient for accurate representation in practical applications, and redundant…
70relevance
12
PPubMed16 Apr 2013
The non-negative matrix factorization toolbox for biological data mining
Yifeng Li, Alioune Ngom
Background Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does…
65relevance
13
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…
64relevance
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…
64relevance
15
PPubMed13 Feb 2015
NMF-mGPU: non-negative matrix factorization on multi-GPU systems
Edgardo Mejía-Roa, Daniel Tabas-Madrid, Javier Setoain, Carlos García + 2 more
'Carlos García' 'Francisco Tirado' 'Alberto Pascual-Montano'] Background In the last few years, the Non-negative Matrix Factorization(NMF) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time…
64relevance
16
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
63relevance
17
BbioRxiv28 Apr 2023
Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization
Kasun Pathirage, Aman Virmani, Alison J. Scott, Richard J. Traub + 4 more
Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. MSI data analysis presents problems due to the large file sizes and computational resource requirements and also due to the complexity of interpreting the raw spectral…
62relevance
18
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…
62relevance
19
PPubMed21 Apr 2008
Nonnegative Matrix Factorization with Gaussian Process Priors
Mikkel N. Schmidt, Hans Laurberg
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance function. This allows us…
62relevance
20
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
58relevance
21
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…
57relevance
22
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
53relevance
23
CchemRxiv6 Oct 2025
Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
52relevance

Results for graph‑regularized NMF

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

A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering

Graph Regularized NMF with L20-norm for Unsupervised Feature Learning

Learning manifold to regularize nonnegative matrix factorization

Non-Negative Matrix Factorization with Scale Data Structure Preservation

Supervised Class-pairwise NMF for Data Representation and Classification

Log-based Sparse Nonnegative Matrix Factorization for Data Representation

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding

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

Subspace Nonnegative Matrix Factorization for Feature Representation

The non-negative matrix factorization toolbox for biological data mining

Non-negative matrix factorization and deconvolution as dual simplex problem

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

NMF-mGPU: non-negative matrix factorization on multi-GPU systems

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization

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

Nonnegative Matrix Factorization with Gaussian Process Priors

Multi-fidelity machine learning models for improved high-throughput screening predictions

Theorems on Positive Data: On the Uniqueness of NMF

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

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

A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering

Graph Regularized NMF with L20-norm for Unsupervised Feature Learning

Learning manifold to regularize nonnegative matrix factorization

Non-Negative Matrix Factorization with Scale Data Structure Preservation

Supervised Class-pairwise NMF for Data Representation and Classification

Log-based Sparse Nonnegative Matrix Factorization for Data Representation

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding

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

Subspace Nonnegative Matrix Factorization for Feature Representation

The non-negative matrix factorization toolbox for biological data mining

Non-negative matrix factorization and deconvolution as dual simplex problem

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

NMF-mGPU: non-negative matrix factorization on multi-GPU systems

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization

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

Nonnegative Matrix Factorization with Gaussian Process Priors

Multi-fidelity machine learning models for improved high-throughput screening predictions

Theorems on Positive Data: On the Uniqueness of NMF

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty