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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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A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization [PDF]
, 2013 As we all known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing and signal processing etc.
Lai, Shu-Zhen +2 more
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Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices [PDF]
, 2013 Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of
Gillis, Nicolas
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Generalized Separable Nonnegative Matrix Factorization
, 2019 Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing.
Gillis, Nicolas, Pan, Junjun
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Convex nonnegative matrix factorization with missing data [PDF]
, 2016 International audienceConvex nonnegative matrix factorization (CNMF) is a variant of nonnegative matrix factorization (NMF) in which the components are a convex combination of atoms of a known dictionary.
Emiya, Valentin +2 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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Complexity, 2019 Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of ...
Yong-Jing Hao +4 more
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Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation
Complexity, 2018 Obtaining an optimum data representation is a challenging issue that arises in many intellectual data processing techniques such as data mining, pattern recognition, and gene clustering.
Wei Jiang +4 more
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Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
, 2018 Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components ...
Hook, James +2 more
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Advanced Science, EarlyView.Our study identifies the HDACs‐STAT3 axis as key regulator for M2 macrophage accumulation in DLBCL. We developed Chid@M2pep‐EVs/TP, a pH‐responsive drug delivery system for M2 macrophage specific chidamide administration. By coupling M2‐targeted chidamide with EVs‐mediated delivery, this system reprograms M2 to M1 via HDAC inhibition and STAT3 ...
Bo Dai +15 more
wiley +1 more source