Results 41 to 50 of about 4,116 (162)
Journal of Statistical Theory and Applications (JSTA), 2020 The Probabilistic Latent Semantic Analysis has been related with the Singular Value Decomposition. Several problems occur when this comparative is done.
Pau Figuera Vinué +1 more
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Reverse annealing for nonnegative/binary matrix factorization.
PLoS ONE, 2021 It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly ...
John Golden, Daniel O'Malley
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Optimization of identifiability for efficient community detection
New Journal of Physics, 2020 Many physical and social systems are best described by networks. And the structural properties of these networks often critically determine the properties and function of the resulting mathematical models.
Hui-Jia Li +3 more
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Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing
Remote Sensing, 2020 As a powerful blind source separation tool, Nonnegative Matrix Factorization (NMF) with effective regularizations has shown significant superiority in spectral unmixing of hyperspectral remote sensing images (HSIs) owing to its good physical ...
Wenhong Wang, Hongfu Liu
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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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A multilevel approach for nonnegative matrix factorization [PDF]
Journal of Computational and Applied Mathematics, 2012 Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing, computational biology, etc. In this paper, we explain how algorithms for NMF can be embedded into the
Nicolas Gillis, François Glineur
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Smoothed separable nonnegative matrix factorization
Linear Algebra and its Applications, 2023 31 pages + 10 pages of supplementary. Many clarifications have been brought to the paper, and we have added numerical experiments on facial ...
Nadisic, Nicolas +2 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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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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Multi-Component Nonnegative Matrix Factorization [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand ...
Wang, Jing +8 more
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