Discriminant projective non-negative matrix factorization. [PDF]
PLoS ONE, 2013 Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan +4 more
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Co-sparse Non-negative Matrix Factorization [PDF]
Frontiers in Neuroscience, 2022 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.
Fan Wu +3 more
doaj +6 more sources
Optimization and expansion of non-negative matrix factorization [PDF]
BMC Bioinformatics, 2020 Background Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics.
Xihui Lin, Paul C. Boutros
doaj +6 more sources
Musical instrument classification using non-negative matrix factorization algorithms [PDF]
, 2006 In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 ...
Benetos, E, Kotropoulos, C, Kotti, M
core +4 more sources
Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. [PDF]
Brief Bioinform, 2022 The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field.
Hamamoto R +14 more
europepmc +2 more sources
Graph regularized non-negative matrix factorization with $$L_{2,1}$$ L 2 , 1 norm regularization terms for drug–target interactions prediction [PDF]
BMC Bioinformatics, 2023 Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of
Junjun Zhang, Minzhu Xie
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Biclustering of gene expression data by non-smooth non-negative matrix factorization [PDF]
BMC Bioinformatics, 2006 Background The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions.
Carazo Jose M +4 more
doaj +2 more sources
ACCOUNTING FOR PHASE CANCELLATIONS IN NON-NEGATIVE MATRIX FACTORIZATION USING WEIGHTED DISTANCES [PDF]
, 2014 (c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Ewert, S, IEEE, Plumbley, MD, Sandler, M
core +3 more sources
Blind source separation with optimal transport non-negative matrix factorization
EURASIP Journal on Advances in Signal Processing, 2018 Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF ...
Antoine Rolet +3 more
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Bayesian multi-study non-negative matrix factorization for mutational signatures [PDF]
Genome BiologyMutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across ...
Isabella N. Grabski +2 more
doaj +2 more sources