Results 11 to 20 of about 1,698,574 (297)
Nonnegativity Problems for Matrix Semigroups
CoRR, 2023 The matrix semigroup membership problem asks, given square matrices $M,M_1,\ldots,M_k$ of the same dimension, whether $M$ lies in the semigroup generated by $M_1,\ldots,M_k$. It is classical that this problem is undecidable in general but decidable in case $M_1,\ldots,M_k$ commute. In this paper we consider the problem of whether, given $M_1,\ldots,M_k$
D'Costa, Julian +2 more
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Randomized nonnegative matrix factorization [PDF]
Pattern Recognition Letters, 2018 This is an extended and revised version of the paper which appeared in ...
N. Benjamin Erichson +3 more
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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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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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Coseparable Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and Applications, 2023 Abstract. Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. The aim is to find a low rank approximation for nonnegative matrix [Formula: see text] by a product of two nonnegative matrices [Formula: see text] and [Formula: see text]. In general, NMF is NP-hard to solve while it can be solved
Junjun Pan, Michael Ng 0001
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Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation. [PDF]
PLoS ONE, 2015 Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable.
Shota Saito +3 more
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Co-sparse Non-negative Matrix Factorization
Frontiers in Neuroscience, 2022 Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu +3 more
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Transductive Nonnegative Matrix Tri-Factorization
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
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Maximal Nonnegative Perturbation of a Nonnegative Matrix [PDF]
, 2006 For a class of positive matrices A + K with a stable positive nominal part A and a structured positive perturbation part K, we address the problem of finding the largest admissible perturbation such that the global matrix remains stable. Theoretical bounds on the size of this set are derived and an algorithm for constructing a set of admissible ...
Haut, Bertrand +2 more
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The Growth of Powers of a Nonnegative Matrix [PDF]
SIAM Journal on Algebraic Discrete Methods, 1980 Let A be a nonnegative $n \times n$ matrix. In this paper we study the growth of the powers $A^m, m = 1,2,3, \cdots $ when $\rho ( A ) = 1$. These powers occur naturally in the iteration process \[x^{( m + 1 )} = Ax^{( m )} ,\quad x^{( 0 )} \geqq 0,\] which is important in applications and numerical techniques.
Shmuel Friedland, Hans Schneider
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