Fairer non-negative matrix factorization [PDF]
Frontiers in Big DataThere has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical
Lara Kassab +5 more
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In-memory analog computing for non-negative matrix factorization [PDF]
Nature CommunicationsNon-negative matrix factorization (NMF) is a powerful technique for extracting latent structures from high-dimensional data, with applications spanning recommender systems, bioinformatics, and image processing.
Shiqing Wang +6 more
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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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JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification [PDF]
PeerJThe spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures.
Juan Liang +4 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
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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
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Co-sparse Non-negative Matrix Factorization
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
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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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Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 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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