Results 21 to 30 of about 2,151 (154)
IEEE Access, 2018 Nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data.
Wenjie Zhu, Yunhui Yan
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Frontiers in Genetics, 2021 Advances in single cell transcriptomics have allowed us to study the identity of single cells. This has led to the discovery of new cell types and high resolution tissue maps of them.
Sooyoun Oh, Haesun Park, Xiuwei Zhang
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Discriminative and Graph Regularized Nonnegative Matrix Factorization with Kernel Method
Jisuanji kexue yu tansuo, 2020 Nonnegative matrix factorization (NMF) is a popular technique for dimension reduction,which has been extensively applied in image clustering and other fields.However,NMF is an unsupervised approach,which does not take the label information of the data ...
LI Xiangli, ZHANG Ying
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Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering
IEEE Access, 2021 Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the ...
Weiyu Guo
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Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing
Remote Sensing, 2017 Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing.
Risheng Huang, Xiaorun Li, Liaoying Zhao
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Entropy, 2017 Semi-Nonnegative Matrix Factorization (Semi-NMF), as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF still
Peng Luo, Jinye Peng
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IEEE Access, 2020 Community detection is a critical issue in the field of complex networks. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks.
Hong Lu +3 more
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Robust Graph Regularized Nonnegative Matrix Factorization
IEEE Access, 2022 Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on
Qi Huang +3 more
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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng +5 more
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Big Data Mining and AnalyticsNonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.
Guosheng Cui +3 more
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