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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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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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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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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Non-negative Matrix Factorization for Binary Data [PDF]
Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015 We propose the Logistic Non-negative Matrix Factorization for decomposition of binary data. Binary data are frequently generated in e.g. text analysis, sensory data, market basket data etc. A common method for analysing non-negative data is the Non-negative Matrix Factorization, though this is in theory not appropriate for binary data, and thus we ...
Jacob Søgaard Larsen +1 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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Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation [PDF]
EntropyClustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity.
Ling Zhong, Haiyan Gao
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Non‐negative Matrix Factorization
, 2023 International audience ; Solving a source separation problem when the data are explained by a linear mixing of non-negative sources with non-negative mixing coefficients reduces to performing a non-negative factorization (NMF) of the data matrix. This chapter addresses the concept of NMF, discusses some of its geometrical aspects, presents the model ...
Brie, David +2 more
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Multi-view clustering via multi-manifold regularized non-negative matrix factorization
Neural Networks, 2017 Linlin Zong +2 more
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