Results 71 to 80 of about 82,948 (222)
Mathematics, 2021 Nonnegative Matrix Factorization (NMF) has acquired a relevant role in the panorama of knowledge extraction, thanks to the peculiarity that non-negativity applies to both bases and weights, which allows meaningful interpretations and is consistent with ...
Flavia Esposito
semanticscholar +1 more source
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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Fairer non-negative matrix factorization
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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IEEE Transactions on Big Data, 2021 Community detection is of great help to understand the structures and functions of complex networks. It has become one of popular research topics in the field of complex networks analysis.
Chaobo He +5 more
semanticscholar +1 more source
Partial Identifiability for Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and Applications, 2023 27 pages, 8 figures, 7 examples. This third version makes minor modifications. Paper accepted in SIAM J.
Nicolas Gillis, Róbert Rajkó
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IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
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Latent Multi-View Semi-Nonnegative Matrix Factorization with Block Diagonal Constraint
Axioms, 2022 Multi-view clustering algorithms based on matrix factorization have gained enormous development in recent years. Although these algorithms have gained impressive results, they typically neglect the spatial structures that the latent data representation ...
Lin Yuan +3 more
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Mathematics, 2023 NONNEGATIVE matrix factorization (NMF) is an effective technique for dimensionality reduction of high-dimensional data for tasks such as machine learning and data visualization.
Jie Li, Yaotang Li, Chaoqian Li
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A new Approach for Building Recommender System Using Non-Negative Matrix Factorization Method
پژوهشهای ریاضی, 2021 Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is decomposed into components that are more interrelated and divide the data into sections where ...
nushin shahrokhi, somayeh arabi narie
doaj
Heuristics for exact nonnegative matrix factorization [PDF]
Journal of Global Optimization, 2015 32 pages, 2 figures, 16 ...
Arnaud Vandaele +3 more
openaire +5 more sources