Results 41 to 50 of about 14,760 (255)
Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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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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MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization. [PDF]
PLoS ONE, 2015 In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across ...
Olivier Mangin +3 more
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Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
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On Rank Selection in Non-Negative Matrix Factorization Using Concordance
Mathematics, 2023 The choice of the factorization rank of a matrix is critical, e.g., in dimensionality reduction, filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to adjusting the noise, while selecting a rank that is too low ...
Paul Fogel +3 more
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Robust Adaptive Graph Regularized Non-Negative Matrix Factorization
IEEE Access, 2019 Data clustering, which aims to divide the given samples into several different groups, has drawn much attention in recent years. As a powerful tool, non-negative matrix factorization (NMF) has been applied successfully in clustering tasks. However, there
Xiang He, Qi Wang, Xuelong Li
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Non-negative Matrix Factorization: A Survey
The Computer Journal, 2021 AbstractNon-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space.
Gan J, Liu T, Li L, Zhang J
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Fast optimization of non-negative matrix tri-factorization.
PLoS ONE, 2019 Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses ...
Andrej Čopar +2 more
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Reciprocal control of viral infection and phosphoinositide dynamics
FEBS Letters, EarlyView.Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
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FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
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