Results 21 to 30 of about 142,276 (359)

Feature Weighted Non-Negative Matrix Factorization [PDF]

IEEE Transactions on Cybernetics, 2023
Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis. NMF concentrates the features of each sample into a vector, and approximates it by the linear combination of basis vectors, such that the low-dimensional representations ...
Mulin Chen, Maoguo Gong, Xuelong Li
openaire   +3 more sources

Non-negative Matrix Factorization for Dimensionality Reduction [PDF]

ITM Web of Conferences, 2022
—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
doaj   +1 more source

LONMF: a non-negative matrix factorization model based on graph Laplacian and optimal transmission for paired single-cell multi-omics data integration. [PDF]

BMC Bioinformatics
Nan M, Ren Q, Fu Y, Chen X, Qi G, Wang L, Gao J.   +6 more
europepmc   +2 more sources

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

Nat Protoc, 2023
Johnson JAI, Tsang AP, Mitchell JT, Zhou DL, Bowden J, Davis-Marcisak E, Sherman T, Liefeld T, Loth M, Goff LA, Zimmerman JW, Kinny-Köster B, Jaffee EM, Tamayo P, Mesirov JP, Reich M, Fertig EJ, Stein-O'Brien GL.   +17 more
europepmc   +3 more sources

Probabilistic Non-Negative Matrix Factorization with Binary Components

Mathematics, 2021
Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data.
Xindi Ma, Jie Gao, Xiaoyu Liu, Taiping Zhang, Yuanyan Tang   +4 more
doaj   +1 more source

Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

Frontiers in Immunology, 2021
A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug
Xianfang Tang, Lijun Cai, Yajie Meng, Junlin Xu, Changcheng Lu, Jialiang Yang   +5 more
semanticscholar   +1 more source

Non-negative matrix factorization test cases [PDF]

2016 IEEE MIT Undergraduate Research Technology Conference (URTC), 2016
4 pages, 3 figures, to appear in the proceedings of the 2015 IEEE MIT Undergraduate Research ...
Sell, Connor, Kepner, Jeremy
openaire   +2 more sources

Link prediction based on non-negative matrix factorization. [PDF]

PLoS ONE, 2017
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like ...
Bolun Chen, Fenfen Li, Senbo Chen, Ronglin Hu, Ling Chen   +4 more
doaj   +1 more source

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

Computation, 2021
Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed.
Angelica Alejandra Serrano-Rubio, Guillermo B. Morales-Luna, Amilcar Meneses-Viveros   +2 more
doaj   +1 more source

Kernel Joint Non-Negative Matrix Factorization for Genomic Data

IEEE Access, 2021
The multi-modal or multi-view integration of data has generated a wide range of applicability in pattern extraction, clustering, and data interpretation.
Diego Salazar, Juan Rios, Sara Aceros, Oscar Florez-Vargas, Carlos Valencia   +4 more
doaj   +1 more source