Results 81 to 90 of about 82,948 (222)
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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Link prediction based on non-negative matrix factorization. [PDF]
PLoS ONE, 2017 With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like ...
Bolun Chen +4 more
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Max–min distance nonnegative matrix factorization
Neural Networks, 2015 Nonnegative Matrix Factorization (NMF) has been a popular representation method for pattern classification problems. It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basis matrix and a nonnegative coefficient matrix. The columns of the coefficient matrix can be used as new representations of these data samples.
Wang, Jim Jing-Yan, Gao, Xin
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Neighborhood Preserving Convex Nonnegative Matrix Factorization [PDF]
Mathematical Problems in Engineering, 2014 The convex nonnegative matrix factorization (CNMF) is a variation of nonnegative matrix factorization (NMF) in which each cluster is expressed by a linear combination of the data points and each data point is represented by a linear combination of the cluster centers.
Wei, Jiang, Min, Li, Zhang, Yongqing
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Stretched non-negative matrix factorization
npj Computational MaterialsA novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu +11 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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Robust semi-supervised nonnegative matrix factorization [PDF]
2015 International Joint Conference on Neural Networks (IJCNN), 2015 Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a wide range of applications such as data clustering, pattern recognition and computer vision. Real-world data are often sparse and noisy which may reduce the accuracy of representations. And a small part of data may
Wang, Jing +3 more
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Context aware nonnegative matrix factorization clustering [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 6 pages, 3 figures.
TRIPODI, ROCCO +2 more
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Simplicial nonnegative matrix factorization
The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2013 Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mining, especially for dimension reduction and component analysis. It is employed widely in different fields such as information retrieval, image processing, etc. After a decade of fast development, severe limitations still remained in NMFs methods including high ...
null Duy Khuong Nguyen +2 more
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Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
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