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Generalized Separable Nonnegative Matrix Factorization Algorithm Based on Orthogonal Constraints [PDF]
Jisuanji gongcheng, 2023 Separable Nonnegative Matrix Factorization(NMF) is a special NMF method used to represent an entire dataset by extracting partial samples or key topics from the dataset.Generalized Separable Nonnegative Matrix Factorization(GSNMF) is an extended ...
Junhang CHEN, Zuyuan YANG, Mingyang LIU, Lingjiang LI
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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
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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
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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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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 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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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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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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