Results 11 to 20 of about 17,677 (287)
ComTech, 2012 NMF is one new developed method to make the part-based representation of non-negative data, such as human face image. NMF can reduce the dimension of high dimensional data such as multimedia data. In many researches,NMF can also used as a classification
William Salim
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Stratified-NMF for Heterogeneous Data
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023 5 pages.
James Chapman 0007 +2 more
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Procedings of the British Machine Vision Conference 2011, 2011 In this paper, we propose a maximum-margin framework for classification using Non-negative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization are separated, we incorporate the maximum margin constraints within the NMF formuation i.e.
B. G. Vijay Kumar +2 more
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NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration [PDF]
Journal of Bioinformatics and Systems Biology, 2023 Abstract Summary Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret.
Liefeld, Ted +7 more
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Clays and Clay Minerals, 1993 AbstractBulk and size-fractionated kaolinites from seven localities in Australia as well as the Clay Minerals Society Source Clays Georgia KGa-1 and KGa-2 have been studied by X-ray diffraction (XRD), laser scattering, and electron microscopy in order to understand the variation of particle characteristics across a range of environments and to ...
Uwins, P.J.R. +3 more
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Application of spatially related MRF model in NMF hyperspectral unmixing
MATEC Web of Conferences, 2018 Aiming at Non-negative Matrix Factorization (NMF)’s problem of initialization and "local minima" in hyperspectral unmixing, a NMF linear unmixing algorithm with spatial correlation constrains (SCNMF) based on Markov Random Field (MRF) was proposed ...
Yuan Bo
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Chordal-NMF with Riemannian Multiplicative Update
CoRR25 pages, 7 figures, 2 ...
Esposito, Flavia, Ang, Andersen
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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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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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, 2011 Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm ...
Potluru, Vamsi K. +4 more
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