Results 11 to 20 of about 7,807 (178)
Linking Cortical Morphometry in Self-Limited Epilepsy With Centrotemporal Spikes to Cognition, Function, and Molecular Architecture. [PDF]
CNS Neurosci TherThis study provides a novel framework modeling neurodevelopment in self‐limited epilepsy with centrotemporal spikes. We identify eight biologically interpretable brain structural deviation patterns, which are linked to clinical phenotypes and molecular profiles involving cognition and mitochondrial function. This integration elucidates multidimensional
Yang S +6 more
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Sensors, 2021 In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle
Yehao Ma +5 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral spectral mixture analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote ...
Ge Zhang, Shaohui Mei, Yan Feng, Qian Du
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CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)
, 2023 Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering
Mhammedi, Sajida +3 more
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Using underapproximations for sparse nonnegative matrix factorization [PDF]
, 2008 Nonnegative Matrix Factorization (NMF) has gathered a lot of attention in the last decade and has been successfully applied in numerous applications.
GILLIS, Nicolas, GLINEUR, François
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Adaptive Kernel Graph Nonnegative Matrix Factorization
Information, 2023 Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized ...
Rui-Yu Li, Yu Guo, Bin Zhang
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Adaptive computation of the Symmetric Nonnegative Matrix Factorization (SymNMF)
SeMA Journal, 2020 Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is ...
P. Favati +3 more
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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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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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Jisuanji kexue yu tansuo, 2020 In order to handle semi-supervised clustering scenarios where only part of the pairwise constraint information is available in the target dataset, on the basis of nonnegative matrix factorization (NMF) architecture, this paper proposes a nonnegative ...
CAO Jiawei, QIAN Pengjiang
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