Results 31 to 40 of about 138,910 (273)
Novel Algorithms Based on Majorization Minimization for Nonnegative Matrix Factorization
IEEE Access, 2019 Matrix decomposition is ubiquitous and has applications in various fields like speech processing, data mining and image processing to name a few. Under matrix decomposition, nonnegative matrix factorization is used to decompose a nonnegative matrix into ...
R. Jyothi, Prabhu Babu, Rajendar Bahl
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Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering
International Journal of Computational Intelligence Systems, 2020 Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion.
Xiangguang Dai +3 more
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Using Underapproximations for Sparse Nonnegative Matrix Factorization [PDF]
, 2008 Nonnegative Matrix Factorization consists in (approximately) factorizing a nonnegative data matrix by the product of two low-rank nonnegative matrices. It has been successfully applied as a data analysis technique in numerous domains, e.g., text mining ...
Anstreicher +24 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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Online kernel nonnegative matrix factorization [PDF]
Signal Processing, 2017 Nonnegative matrix factorization (NMF) has become a prominent signal processing and data analysis technique. To address streaming data, online methods for NMF have been introduced recently, mainly restricted to the linear model. In this paper, we propose a framework for online nonlinear NMF, where the factorization is conducted in a kernel-induced ...
Zhu, Fei, Honeine, Paul
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Smoothed separable nonnegative matrix factorization
Linear Algebra and its Applications, 2023 31 pages + 10 pages of supplementary. Many clarifications have been brought to the paper, and we have added numerical experiments on facial ...
Nicolas Nadisic +2 more
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Sufficient conditions to be exceptional
Special Matrices, 2016 A copositive matrix A is said to be exceptional if it is not the sum of a positive semidefinite matrix and a nonnegative matrix. We show that with certain assumptions on A−1, especially on the diagonal entries, we can guarantee that a copositive matrix A
Johnson Charles R., Reams Robert B.
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Adaptive Kernel Graph Nonnegative Matrix Factorization
Information, 2023 Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized ...
Rui-Yu Li, Yu Guo, Bin Zhang
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Nonnegative Matrix Factorization [PDF]
, 2013 Matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data. SVD is a classical method for matrix factorization, which gives the optimal low-rank approximation to a real-valued matrix in terms of the squared error.
Ke-Lin Du, M. N. S. Swamy
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Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
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