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Coseparable Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and Applications, 2023 Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. It aims to find a low rank approximation for nonnegative data M by a product of two nonnegative matrices W and H. In general, NMF is NP-hard to solve while it can be solved efficiently under separability assumption, which requires the columns of factor matrix
Junjun Pan, Michael Ng 0001
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Continuous Semi-Supervised Nonnegative Matrix Factorization
Algorithms, 2023 Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom +4 more
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On Rationality of Nonnegative Matrix Factorization [PDF]
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017 Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. NMF has a wide variety of applications, including bioinformatics, chemometrics, communication complexity, machine learning, polyhedral combinatorics, among many others ...
Dmitry Chistikov 0001 +4 more
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Matrix Factorization as Search [PDF]
, 2012 Simplex Volume Maximization (SiVM) exploits distance geometry for efficiently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques.
Kristian Kersting +3 more
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Negative Binomial Matrix Factorization [PDF]
IEEE Signal Processing Letters, 2020 We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF
Olivier Gouvert +2 more
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BMC Bioinformatics, 2022 Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching
Junjun Zhang, Minzhu Xie
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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 +4 more
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IEEE Access, 2021 Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of
Xuan Hu, Yongming Han, Zhiqiang Geng
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Cauchy nonnegative matrix factorization [PDF]
2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015 Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for nonnegative data. It enjoys a rich background, both from an optimization and probabilistic signal processing viewpoint. In this study, we propose a new cost-function for NMF fitting, which is introduced as arising naturally when adopting a Cauchy process model for ...
Liutkus, Antoine +2 more
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Quaternion Matrix Factorization for Low-Rank Quaternion Matrix Completion
Mathematics, 2023 The main aim of this paper is to study quaternion matrix factorization for low-rank quaternion matrix completion and its applications in color image processing.
Jiang-Feng Chen +3 more
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