Results 21 to 30 of about 7,558 (300)
Note on nonnegative matrices [PDF]
Proceedings of the American Mathematical Society, 1970 Let A A be a nonnegative square matrix and
openaire +1 more source
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
doaj +1 more source
Transductive Nonnegative Matrix Tri-Factorization
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
doaj +1 more source
Matrix Analysis for Continuous-Time Markov Chains
Special Matrices, 2021 Continuous-time Markov chains have transition matrices that vary continuously in time. Classical theory of nonnegative matrices, M-matrices and matrix exponentials is used in the literature to study their dynamics, probability distributions and other ...
Le Hung V., Tsatsomeros M. J.
doaj +1 more source
Minimal positive realizations of transfer functions with nonnegative multiple poles [PDF]
, 2005 This note concerns a particular case of the minimality problem in positive system theory. A standard result in linear system theory states that any nth-order rational transfer function of a discrete time-invariant linear single-input-single-output (SISO)
Matolcsi, Máté, Nagy, B.
core +1 more source
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
doaj +1 more source
A Note on NIEP for Leslie and Doubly Leslie Matrices
Mathematics, 2020 The nonnegative inverse eigenvalue problem (NIEP) consists of finding necessary and sufficient conditions for the existence of a nonnegative matrix with a given list of complex numbers as its spectrum.
Luis Medina, Hans Nina, Elvis Valero
doaj +1 more source
The Sinkhorn-Knopp algorithm : convergence and applications [PDF]
, 2008 As long as a square nonnegative matrix A contains sufficient nonzero elements, then the Sinkhorn-Knopp algorithm can be used to balance the matrix, that is, to find a diagonal scaling of A that is doubly stochastic.
Knight, P.A.
core +1 more source
Which nonnegative matrices are slack matrices?
Linear Algebra and its Applications, 2013 In this paper we characterize the slack matrices of cones and polytopes among all nonnegative matrices. This leads to an algorithm for deciding whether a given matrix is a slack matrix. The underlying decision problem is equivalent to the polyhedral verification problem whose complexity is unknown.
Gouveia, João +5 more
openaire +4 more sources
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
doaj +1 more source