Results 271 to 280 of about 1,698,574 (297)

Unilateral Orthogonal Nonnegative Matrix Factorization

SIAM Journal on Control and Optimization, 2023
Abstract. A type of matrix decomposition called unilateral orthogonal nonnegative matrix factorization is proposed for decomposing real-valued matrices. The associated optimization problem is nonconvex and nonlinear. We design an iterative algorithm based on alternating minimization, where a data compensation mechanism is used to ...
Jun Shang, Tongwen Chen
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Nonnegative matrix factorization with matrix exponentiation

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Nonnegative 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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Elastic nonnegative matrix factorization

Pattern Recognition, 2019
Abstract Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust l2,1-norm to measure the quality of factorization, given the assumption of i.i.d Gaussian noise model and i.i.d Laplacian noise model, respectively.
He Xiong, Deguang Kong
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Parallelism on the Nonnegative Matrix Factorization

2012
A great interest has been given to the Nonnegative Matrix Factorization (NMF) due to its ability of extracting highly-interpretable parts from data sets. Nonetheless, its usage is hindered by the computational complexity when processing large matrices.
Edgardo Mejía-Roa, Carlos García 0001, José Ignacio Gómez, Manuel Prieto 0001, Christian Tenllado, Alberto D. Pascual-Montano, Francisco Tirado   +6 more
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Nonnegative Rank Factorization of a Nonnegative Matrix A with A A ≥0

Linear and Multilinear Algebra, 2003
In this article we obtain a nonnegative rank factorization of nonnegative matrices A satisfying one or both of the following conditions: (i) AA † ⩽ 0 (ii) A † A ⩽ 0, thus providing a new set of conditions that guarantee the existence of a nonnegative least-squares solution of a linear system.
S.K. Jain, John Tynan
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On Nonnegative Solutions of Matrix Equations

SIAM Journal on Algebraic Discrete Methods, 1985
Let ${\bf A}$ be a nonnegative and nontrivial $n \times n$ matrix. In this work, we present necessary and sufficient conditions for the matrix equation, $( \lambda {\bf I} - {\bf A} ) x = b$, to possess a nonnegative solution x whenever b is a given nonnegative and nontrivial vector and $\lambda$ is any given positive parameter. This analysis extends a
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Nonnegative Matrix Factorization

Proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation, 2015
How quickly can we compute the nonnegative rank (r) of an m x n matrix? This problem ---- and the companion problem of finding a nonnegative matrix factorization with minimum inner-dimension ---- has a rich history, with applications in quantum mechanics, probability theory, data analysis, communication complexity and polyhedral combinatorics.
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Generalized Separable Nonnegative Matrix Factorization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Nicolas Gillis, Junjun Pan
exaly  

Convex and Semi-Nonnegative Matrix Factorizations

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
Michael I. Jordan, Chris H Q Ding, Chris Ding   +2 more
exaly  

Constrained Nonnegative Matrix Factorization for Image Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Thomas Huang, Deng Cai, Xuelong Li
exaly