Results 11 to 20 of about 1,535 (215)
Non-negative Matrix Factorization: A Survey
The Computer Journal, 2021 AbstractNon-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space.
Gan J, Liu T, Li L, Zhang J
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Shifted Non-Negative Matrix Factorization [PDF]
2007 IEEE Workshop on Machine Learning for Signal Processing, 2007 Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be ...
Mørup, Morten; id_orcid 0000-0003-4985-4368 +2 more
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Bayesian Non-negative Matrix Factorization [PDF]
, 2009 We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates.
Mikkel N. Schmidt +2 more
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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 +4 more
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A Non-Negative Matrix Factorization Game
CoRR, 2021 We present a novel game-theoretic formulation of Non-Negative Matrix Factorization (NNMF), a popular data-analysis method with many scientific and engineering applications. The game-theoretic formulation is shown to have favorable scaling and parallelization properties, while retaining reconstruction and convergence performance comparable to the ...
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Rank selection for non‐negative matrix factorization
Statistics in Medicine, 2023 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 and the other is the coefficients matrix which is the projections of data points to the new basis.
Yun Cai, Hong Gu, Toby Kenney
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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 +4 more
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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 +2 more
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Scalable non-negative matrix tri-factorization
BioData Mining, 2017 Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining.
Andrej Čopar +2 more
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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 ...
Connor Sell, Jeremy Kepner
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