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Elastic nonnegative matrix factorization
Pattern Recognition, 2019Abstract 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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Nonnegative matrix factorization with matrix exponentiation
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Nonnegative 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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Variational Nonnegative Matrix Factorisation
2009 IEEE 17th Signal Processing and Communications Applications Conference, 2009We describe non-negative matrix factorisation (NMF) in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to standard NMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the Expectation-Maximisation (EM) algorithm ...
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The biofilm matrix: multitasking in a shared space
Nature Reviews Microbiology, 2022Hans-Curt Flemming +2 more
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Matrix product states and projected entangled pair states: Concepts, symmetries, theorems
Reviews of Modern Physics, 2021Juan Ignacio Cirac Sasturáin +2 more
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