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Elastic Nonnegative Matrix Factorization
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018Nonnegative Matrix Factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process.
Peter Ballen, Sudipto Guha
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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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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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Parallelism on the Nonnegative Matrix Factorization
2012A 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 +6 more
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Nonsmooth nonnegative matrix factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an ...
Alberto D. Pascual-Montano +4 more
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The Nonnegative Matrix Factorization: Regularization and Complexity
SIAM Journal on Scientific Computing, 2016Summary: Data continues to grow, and it has become ever important to find effective big data analysis techniques. Computational tools, such as singular value decomposition, have been employed in the interpretation of big data. Another tool has recently gained popularity and comparative success: the nonnegative matrix factorization (NMF). The NMF method
Kazufumi Ito, A. K. Landi
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Nonnegative Matrix Factorization
Proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation, 2015How 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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Document clustering using nonnegative matrix factorization
Information Processing and Management, 2006Michael W Berry +2 more
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Algorithms and applications for approximate nonnegative matrix factorization
Computational Statistics and Data Analysis, 2007Michael W Berry +2 more
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