Results 31 to 40 of about 137,800 (236)
On Restricted Nonnegative Matrix Factorization [PDF]
, 2016 Full version of an ICALP'16 ...
Chistikov, D +4 more
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Sparse Separable Nonnegative Matrix Factorization [PDF]
, 2021 We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor are equal to columns of the input matrix, while sparsity requires that the columns of the second NMF factor are sparse.
Nicolas Nadisic +3 more
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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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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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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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Computing approximate PSD factorizations [PDF]
, 2016 We give an algorithm for computing approximate PSD factorizations of nonnegative matrices. The running time of the algorithm is polynomial in the dimensions of the input matrix, but exponential in the PSD rank and the approximation error.
Basu, Amitabh, Dinitz, Michael, Li, Xin
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On visualisation scaling, subeigenvectors and Kleene stars in max algebra [PDF]
, 2009 The purpose of this paper is to investigate the interplay arising between max algebra, convexity and scaling problems. The latter, which have been studied in nonnegative matrix theory, are strongly related to max algebra.
Afriat +36 more
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Two cores of a nonnegative matrix
Linear Algebra and its Applications, 2013 The layout of the paper has been changed; numerous minor changes and ...
Bit-Shun Tam +3 more
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The Generalized Inverse of a Nonnegative Matrix [PDF]
Proceedings of the American Mathematical Society, 1972 Necessary and sufficient conditions are given in order that a nonnegative matrix have a nonnegative MoorePenrose generalized inverse.
Randall E. Cline, Robert J. Plemmons
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