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Algorithms for Nonnegative Matrix Factorization with the Kullback–Leibler Divergence
Journal of Scientific Computing, 2020Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction technique for nonnegative data sets. In order to measure the discrepancy between the input data and the low-rank approximation, the Kullback–Leibler (KL) divergence is ...
L. Hien, Nicolas Gillis
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IEEE Transactions on Industrial Informatics, 2020
Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns.
Yan Song +4 more
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Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns.
Yan Song +4 more
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Improved Clutter Removal in GPR by Robust Nonnegative Matrix Factorization
IEEE Geoscience and Remote Sensing Letters, 2020The clutter encountered in the ground-penetrating radar (GPR) system severely decreases the visibility of subsurface objects, thus highly degrading the performance of the target detection algorithms.
D. Kumlu, I. Erer
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Nonnegative Matrix Factorization With Regularizations
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2014Matrix factorization techniques have been frequently applied in many fields. Among them, nonnegative matrix factorization (NMF) has received considerable attention for it aims to find a parts-based, linear representations of nonnegative data. Recently, many researchers propose various manifold learning algorithms to enhance learning performance by ...
Weiya Ren, Guohui Li, Dan Tu, Li Jia
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Regularized nonnegative matrix factorization with adaptive local structure learning
Neurocomputing, 2020Due to the effectiveness of Nonnegative Matrix Factorization (NMF) and its graph regularized extensions, these methods have been received much attention from various researchers.
Shudong Huang +3 more
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Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2020The presence of mixed pixels in the hyperspectral data makes unmixing to be a key step for many applications. Unsupervised unmixing needs to estimate the number of endmembers, their spectral signatures, and their abundances at each pixel.
Jiangtao Peng +4 more
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A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection
IEEE Transactions on Industrial Informatics, 2020In the era of big data, data-driven fault detection is vital for modern industrial systems. This article considers the potential complexity of fault detection and proposes a novel nonlinear method based on nonnegative matrix factorization (NMF ...
Zelin Ren +2 more
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Robust orthogonal nonnegative matrix tri-factorization for data representation
Knowledge-Based Systems, 2020Nonnegative matrix factorization (NMF) has been a vital data representation technique, and has demonstrated significant potential in the field of machine learning and data mining.
Siyuan Peng +3 more
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Nonnegative matrix factorization with matrix exponentiation
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Nonnegative matrix factorization (NMF) has been successfully applied to different domains as a technique able to find part-based linear representations for nonnegative data. However, when extra constraints are incorporated into NMF, simple gradient descent optimization can be inefficient for high-dimensional problems, due to the overhead to enforce the
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Cloud removal in remote sensing images using nonnegative matrix factorization and error correction
Isprs Journal of Photogrammetry and Remote Sensing, 2019In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier to the effective observation of sensors. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix ...
Xinghua Li +5 more
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