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23 papers · ranked by Valyu relevance

01
PPubMed15 Jul 2025
GRACKLE: an interpretable matrix factorization approach for biomedical representation learning
Lucas A Gillenwater, Lawrence E Hunter, James C Costello
NMF factorizes a matrix of nonnegative values, $Y \inRm\timesn$, for $ m$ samples and $n$ features, into two matrices $W \inRm\timesk$ and $H \inRk\timesn$, where $k≪\text{min}⁡{m,n}$. such that, $(2)Y∼WH$ The nonnegative constraints decompose the matrix into parts that are additive combinations. Each column in $Y$ is…
96relevance
02
PPubMed6 Sep 2024
Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.
Ziwei Chen, Liangzhe Zhang, Jingyi Li, Hang Chen
Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this…
94relevance
03
PPubMed19 Aug 2025
Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation
Ling Zhong, Haiyan Gao, Friedhelm Schwenker
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during…
92relevance
04
PPubMed28 Jun 2024
Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition
Charles Broadbent, Tianci Song, Rui Kuang
We used three methods based on the factorizations shown in [btae245-F1] as well as NSFH as baseline comparisons, focusing on linear methods that are able to extract spatial components in spatial transcriptomics data so that they can be used for direct quantitative and visual comparison to GraphTucker. 1. Nonnegative…
87relevance
05
AarXiv25 Apr 2022
Graph-Regularized Non-Negative Tensor-Ring Decomposition for Multiway Representation Learning
Yuyuan Yu, Guoxu Zhou, Ning Zheng, Yuning Qiu + 2 more
'Qibin Zhao'] Abstract—Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications. In this paper, nonnegative tensor ring (NTR) decomposition and graph regularized NTR (GNTR) decomposition are…
86relevance
06
BbioRxiv18 May 2022
Detecting cell type from single cell RNA sequencing based on deep bi-stochastic graph regularized matrix factorization
Wei Lan, Jianwei Chen, Qingfeng Chen, Jin Liu + 2 more
The application of fruitful achievement of single-cell RNA-sequencing (scRNA-seq) technology has generated huge amount of gene transcriptome data. It has provided a whole new perspective to analyze the transcriptome at single-cell level. Cluster analysis of scRNA-seq is an efficient approach to reveal unknown…
86relevance
07
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…
85relevance
08
AarXiv21 Oct 2024
Graph Regularized Sparse <i>L</i><sub>2,1</sub> Semi‐Nonnegative Matrix Factorization for Data Reduction
Anthony D. Rhodes, Bin Jiang, Jenny Jiang
Non-negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi-Nonnegative Matrix Factorization (SNF), extends the ability of NMF to render parts-based data representations to…
83relevance
09
AarXiv12 Feb 2025
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
Yao Wang, Yiyang Yang, Kaidong Wang, Gao + 2 more
We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the graph's similarity structure to enhance matrix recovery. Existing approaches, primarily based on graph Laplacian regularization, suffer from several…
83relevance
10
AarXiv2023
A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection
Zhigang Liu, Xin Luo
—Community is a fundamental and critical characteristic of a Large-scale Undirected Network (LUN) like a social network, making community detection a vital yet thorny issue in LUN representation learning. Owing to its good scalability and interpretability, a Symmetric and Nonnegative Matrix Factorization (SNMF) model…
83relevance
11
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…
78relevance
12
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…
78relevance
13
AarXiv30 Apr 2025
Robust Orthogonal NMF with Label Propagation for Image Clustering
Jingjing Liu, Nian Wu, Xianchao Xiu, Jianhua Zhang
Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF methods are highly sensitive to noise corruption and are unable to effectively leverage limited supervised information. To overcome these…
77relevance
14
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…
75relevance
15
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…
72relevance
16
BbioRxiv27 Apr 2025
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
Tissues consist of multi-cellular neighborhoods in which different cell types express correlated gene programs due to shared signaling environments. Methods for identifying these spatial neighborhoods may be powerful, but currently do not scale to existing data sets of millions of cells and often artificially divide…
71relevance
17
BbioRxiv25 Sep 2024
Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data
Zuqi Li, Sam F. L. Windels, Noël Malod-Dognin, Seth M. Weinberg + 7 more
Combining omics and images, can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with…
67relevance
18
BbioRxiv2 Nov 2024
An Interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis
Zexuan Wang, Qipeng Zhan, Shu Yang, Zhuoping Zhou + 3 more
Recent advancements in single-cell omics technologies have enabled detailed characterization of cellular processes. However, coassay sequencing technologies remain limited, resulting in un-paired single-cell omics datasets with differing feature dimensions; we present GROTIA (Graph-Regularized Optimal Transport…
63relevance
19
AarXiv2022
Generalized Laplacian Regularized Framelet Graph Neural Networks
Zhiqi Shao, Andi Han, Shi Dai, Andrey L. Vasnev + 1 more
Graph neural networks (GNNs) have achieved remarkable results for various graph learning tasks. However, one of the recent challenges for GNNs is to adapt to different types of graph inputs, such as heterophilic graph datasets in which linked nodes are more likely to contain a different class of labels and features.…
63relevance
20
BbioRxiv24 Feb 2025
Nearest-neighbor nonnegative spatial factorization to study spatial and temporal transcriptomics
Priyanka Shrestha, Luis Chumpitaz Diaz, Barbara E Engelhardt
Nonnegative spatial factorization (NSF) is a spatially-aware factorization method that uses Gaussian processes (GPs) as spatial priors in a Poisson latent factor model to robustly identify interpretable, parts-based representations in spatial transcriptomics data. However, NSF scales poorly with modern datasets due to…
62relevance
21
CchemRxiv14 Sep 2022
Noise Reduction of Low Count STEM-EDX Data by Low-rank Regularized Spectral Smoothing
Keisuke Ozawa
Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of…
60relevance
22
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…
57relevance
23
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…
57relevance

Results for graph regularized nonnegative matrix factorization

GRACKLE: an interpretable matrix factorization approach for biomedical representation learning

Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation

Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

Graph-Regularized Non-Negative Tensor-Ring Decomposition for Multiway Representation Learning

Detecting cell type from single cell RNA sequencing based on deep bi-stochastic graph regularized matrix factorization

Graph Regularized NMF with L20-norm for Unsupervised Feature Learning

Graph Regularized Sparse <i>L</i><sub>2,1</sub> Semi‐Nonnegative Matrix Factorization for Data Reduction

Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach

A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection

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

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Robust Orthogonal NMF with Label Propagation for Image Clustering

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

Non-negative matrix factorization and deconvolution as dual simplex problem

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

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

An Interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis

Generalized Laplacian Regularized Framelet Graph Neural Networks

Nearest-neighbor nonnegative spatial factorization to study spatial and temporal transcriptomics

Noise Reduction of Low Count STEM-EDX Data by Low-rank Regularized Spectral Smoothing

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

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

GRACKLE: an interpretable matrix factorization approach for biomedical representation learning

Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation

Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

Graph-Regularized Non-Negative Tensor-Ring Decomposition for Multiway Representation Learning

Detecting cell type from single cell RNA sequencing based on deep bi-stochastic graph regularized matrix factorization

Graph Regularized NMF with L20-norm for Unsupervised Feature Learning

Graph Regularized Sparse <i>L</i><sub>2,1</sub> Semi‐Nonnegative Matrix Factorization for Data Reduction

Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach

A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection

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

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Robust Orthogonal NMF with Label Propagation for Image Clustering

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

Non-negative matrix factorization and deconvolution as dual simplex problem

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

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

An Interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis

Generalized Laplacian Regularized Framelet Graph Neural Networks

Nearest-neighbor nonnegative spatial factorization to study spatial and temporal transcriptomics

Noise Reduction of Low Count STEM-EDX Data by Low-rank Regularized Spectral Smoothing

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

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