Results 1 to 10 of about 2,011 (138)
Collaborative filtering based on nonnegative/binary matrix factorization [PDF]
Frontiers in Big DataCollaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui +5 more
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Robust Structured Convex Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2021 Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning. Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix.
Qing Yang +3 more
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Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 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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Transductive Nonnegative Matrix Tri-Factorization
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Data representation using robust nonnegative matrix factorization for edge computing
Mathematical Biosciences and Engineering, 2022 As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition.
Qing Yang, Jun Chen, Najla Al-Nabhan
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 The fusion of high-resolution multispectral image (HrMSI) and low-resolution hyperspectral image (LrHSI) has been acknowledged as a promising method for generating a high-resolution hyperspectral image (HrHSI), which is also termed to be an essential part for precise recognition and cataloguing of the underlying materials.
Abdolraheem Khader +2 more
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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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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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