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Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 Michael I Jordan, Chris Ding
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Continuous Semi-Supervised Nonnegative Matrix Factorization
Algorithms, 2023 Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom +4 more
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Nonnegative Inverse Elementary Divisors Problem for Lists with Nonnegative Real Parts
Mathematics, 2020 In this paper, sufficient conditions for the existence and construction of nonnegative matrices with prescribed elementary divisors for a list of complex numbers with nonnegative real part are obtained, and the corresponding nonnegative matrices are ...
Hans Nina +3 more
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Uncovering community structures with initialized Bayesian nonnegative matrix factorization. [PDF]
PLoS ONE, 2014 Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks.
Xianchao Tang +3 more
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Mathematics, 2021 Nonnegative Matrix Factorization (NMF) has acquired a relevant role in the panorama of knowledge extraction, thanks to the peculiarity that non-negativity applies to both bases and weights, which allows meaningful interpretations and is consistent with ...
Flavia Esposito
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Online Nonnegative Matrix Factorization With Outliers [PDF]
IEEE Transactions on Signal Processing, 2016 We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers.
Renbo Zhao, Vincent Y. F. Tan
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Quantized nonnegative matrix factorization [PDF]
2014 19th International Conference on Digital Signal Processing, 2014 Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of
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Toeplitz nonnegative realization of spectra via companion matrices
Special Matrices, 2019 The nonnegative inverse eigenvalue problem (NIEP) is the problem of finding conditions for the existence of an n × n entrywise nonnegative matrix A with prescribed spectrum Λ = {λ1, . . ., λn}.
Collao Macarena +2 more
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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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Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics
Mathematics, 2023 Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du +3 more
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