Results 51 to 60 of about 2,535,217 (348)

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.
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On the effect of the perturbation of a nonnegative matrix on its Perron eigenvector

, 1982
Elsner L, Johnson CR, Neumann MM. On the effect of the perturbation of a nonnegative matrix on its Perron eigenvector. Czechoslovak Mathematical Journal.
Elsner, Ludwig F., Elsner, Ludwig, Neumann, M., Johnson, Charles R., Johnson, C. R., Neumann, Michael M.   +5 more
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Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology [PDF]

, 2012
Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data.
Clare M. Lee, Vass J. Keith, D R Haggart, Lee Clare M., Mudaliar, MAV, Higham, Desmond, Vass, D.J., Manikhandan A V Mudaliar, Gino Miele, Mudaliar, Manikhandan A.V., Haggart, D. R., Mudaliar, M.A.V., Higham, Desmond J., Haggart D. R., Crowther, Daniel, D. R. Haggart, Lee, C.M., Lee, Clare M., Mudaliar Manikhandan A. V., J Keith Vass, Higham Desmond J., Wolf, C.R., Higham, D.J., Wolf C. Roland, Miele Gino, Mudaliar, Manikhandan A. V., Miele, Geno, Desmond J Higham, Clare M Lee, C. Roland Wolf, Miele, G., Manikhandan A. V. Mudaliar, C Roland Wolf, Crowther Daniel, Vass, J.K., Miele, Gino, J. Keith Vass, Crowther, D., Crowther, Daniel; id_orcid, Daniel Crowther, Vass, J. Keith, Desmond J. Higham, Haggart, D.R., Wolf, C. Roland, Lee, Clare   +44 more
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Simplicial nonnegative matrix factorization

The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2013
Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mining, especially for dimension reduction and component analysis. It is employed widely in different fields such as information retrieval, image processing, etc. After a decade of fast development, severe limitations still remained in NMFs methods including high ...
Duy Khuong Nguyen, Khoat Than, Tu Bao Ho
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Computing a Nonnegative Matrix Factorization---Provably [PDF]

SIAM Journal on Computing, 2012
29 pages, 3 ...
Sanjeev Arora, Rong Ge 0001, Ravi Kannan, Ankur Moitra   +3 more
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Collaborative filtering based on nonnegative/binary matrix factorization

Frontiers in Big Data
Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura, Kazue Kudo, Kazue Kudo   +5 more
doaj   +1 more source

Scalable non-negative matrix tri-factorization

BioData Mining, 2017
Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining.
Andrej Čopar, Marinka žitnik, Blaž Zupan   +2 more
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Discriminant projective non-negative matrix factorization. [PDF]

PLoS ONE, 2013
Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan, Xiang Zhang, Zhigang Luo, Dacheng Tao, Xuejun Yang   +4 more
doaj   +1 more source

Dynamics of products of nonnegative matrices

Extracta Mathematicae, 2022
The aim of this manuscript is to understand the dynamics of products of nonnegative matrices. We extend a well known consequence of the Perron-Frobenius theorem on the periodic points of a nonnegative matrix to products of finitely many nonnegative ...
S. Jayaraman, Y.K. Prajapaty, S. Sridharan   +2 more
doaj  

Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation

, 2022
Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional
Yi Wang, Wang, Yi, Zhao, Xuezhuan, Lingling Li, Zhang, Ji, Di, Jinhong, Kexin Zhang, Jinhong Di, Xuezhuan Zhao, Zhang, Kexin, Ji Zhang, Li, Lingling   +11 more
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