Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation

Search Page

Filters

My NCBI Filters

Results by year

Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1944 1
1966 2
1973 2
1974 1
1975 1
1976 1
1980 1
1981 1
1983 3
1984 1
1986 1
1987 3
1988 2
1989 4
1990 3
1991 3
1992 1
1993 2
1997 4
1998 2
1999 6
2000 5
2001 9
2002 12
2003 16
2004 16
2005 34
2006 36
2007 53
2008 71
2009 58
2010 85
2011 110
2012 123
2013 163
2014 207
2015 226
2016 297
2017 336
2018 423
2019 467
2020 577
2021 666
2022 906
2023 861
2024 341

Text availability

Article attribute

Article type

Publication date

Search Results

5,273 results

Results by year

Filters applied: . Clear all
Page 1
Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.
Ou-Yang L, Lu F, Zhang ZC, Wu M. Ou-Yang L, et al. Brief Bioinform. 2022 Jan 17;23(1):bbab479. doi: 10.1093/bib/bbab479. Brief Bioinform. 2022. PMID: 34864871 Review.
However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such mat …
However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issue …
Entropy Minimizing Matrix Factorization.
Chen M, Li X. Chen M, et al. IEEE Trans Neural Netw Learn Syst. 2023 Nov;34(11):9209-9222. doi: 10.1109/TNNLS.2022.3157148. Epub 2023 Oct 27. IEEE Trans Neural Netw Learn Syst. 2023. PMID: 35294364
Nonnegative matrix factorization (NMF) is a widely used data analysis technique and has yielded impressive results in many real-world tasks. ...However, outliers deviating from the normal data distribution may have large residues and then dominate the objective valu …
Nonnegative matrix factorization (NMF) is a widely used data analysis technique and has yielded impressive results in many rea …
Matrix factorization with neural networks.
Camilli F, Mézard M. Camilli F, et al. Phys Rev E. 2023 Jun;107(6-1):064308. doi: 10.1103/PhysRevE.107.064308. Phys Rev E. 2023. PMID: 37464655
Matrix factorization is an important mathematical problem encountered in the context of dictionary learning, recommendation systems, and machine learning. We introduce a decimation scheme that maps it to neural network models of associative memory and provide a deta …
Matrix factorization is an important mathematical problem encountered in the context of dictionary learning, recommendation sy …
Empirical Bayes Matrix Factorization.
Wang W, Stephens M. Wang W, et al. J Mach Learn Res. 2021;22:120. J Mach Learn Res. 2021. PMID: 37920532 Free PMC article.
Matrix factorization methods, which include Factor analysis (FA) and Principal Components Analysis (PCA), are widely used for inferring and summarizing structure in multivariate data. Many such methods use a penalty or prior distribution to achieve sparse representa …
Matrix factorization methods, which include Factor analysis (FA) and Principal Components Analysis (PCA), are widely used for …
Co-sparse Non-negative Matrix Factorization.
Wu F, Cai J, Wen C, Tan H. Wu F, et al. Front Neurosci. 2022 Jan 12;15:804554. doi: 10.3389/fnins.2021.804554. eCollection 2021. Front Neurosci. 2022. PMID: 35095402 Free PMC article.
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property. ...To this end, we i …
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matr …
Generalized Separable Nonnegative Matrix Factorization.
Pan J, Gillis N. Pan J, et al. IEEE Trans Pattern Anal Mach Intell. 2021 May;43(5):1546-1561. doi: 10.1109/TPAMI.2019.2956046. Epub 2021 Apr 1. IEEE Trans Pattern Anal Mach Intell. 2021. PMID: 31794387
Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation, and hyperspectral unmixing. Given a data matrix M and a factorization rank …
Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as ima …
Matrix factorization with denoising autoencoders for prediction of drug-target interactions.
Sajadi SZ, Zare Chahooki MA, Tavakol M, Gharaghani S. Sajadi SZ, et al. Mol Divers. 2023 Jun;27(3):1333-1343. doi: 10.1007/s11030-022-10492-8. Epub 2022 Jul 23. Mol Divers. 2023. PMID: 35871213
Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with mat …
Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI …
Rank selection for non-negative matrix factorization.
Cai Y, Gu H, Kenney T. Cai Y, et al. Stat Med. 2023 Dec 30;42(30):5676-5693. doi: 10.1002/sim.9934. Epub 2023 Oct 17. Stat Med. 2023. PMID: 37848186
Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data matrix into two lower dimensional non-negative matrices: one is the basis or feature matrix which consists of the variables a …
Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data …
Matrix Factorization-Based Dimensionality Reduction AlgorithmsA Comparative Study on Spectroscopic Profiling Data.
Zhang Y, Jin L, Guo F, Ni X, Zhao Y, Cheng Y, Wang H. Zhang Y, et al. Anal Chem. 2022 Oct 4;94(39):13385-13395. doi: 10.1021/acs.analchem.2c01922. Epub 2022 Sep 21. Anal Chem. 2022. PMID: 36130041
Among the existing DR algorithms, many can be categorized as a matrix factorization (MF) problem, which decomposes the original data matrix X into the product of a low-dimensional matrix W and a dictionary matrix H. First, this paper provides a …
Among the existing DR algorithms, many can be categorized as a matrix factorization (MF) problem, which decomposes the origina …
Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.
Hsu LL, Culhane AC. Hsu LL, et al. Front Oncol. 2020 Jun 23;10:973. doi: 10.3389/fonc.2020.00973. eCollection 2020. Front Oncol. 2020. PMID: 32656082 Free PMC article. Review.
Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix …
Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, …
5,273 results

Searches related to "matrix factorization"