Results 11 to 20 of about 1,628,183 (185)
Decoding Transdiagnostic Subtypes in Emotional Disorders. [PDF]
CNS Neurosci TherUsing nonnegative matrix factorization (NMF) clustering, our transdiagnostic study identified five representative functional networks (RFNs) that capture the heterogeneity of emotional disorders. These RFNs delineated five clinically distinct subtypes, each with unique diagnostic proportions, clinical symptomatology, and brain functional and structural
Wu Y +5 more
europepmc +2 more sources
ACCOUNTING FOR PHASE CANCELLATIONS IN NON-NEGATIVE MATRIX FACTORIZATION USING WEIGHTED DISTANCES [PDF]
, 2014 (c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Ewert, S, Plumbley, MD, Sandler, M, IEEE
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Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing refers to a source separation problem of decomposing a hyperspectral imagery (HSI) to estimate endmembers, and their corresponding abundances.
Heng-Chao Li +3 more
doaj +1 more source
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
doaj +1 more source
Wireless Communications and Mobile Computing, 2022 Nonnegative matrix factorization (NMF), which is aimed at making all elements of the factorization nonnegative and achieving nonlinear dimensional reduction at the same time, is an effective method for solving recommendation system problems. However, in many real‐world applications, most models learn recommendation models under the supervised learning ...
Ronghua Zhang +5 more
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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
doaj +1 more source
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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