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An efficient nonnegative matrix factorization approach in flexible kernel space
Daoqiang Zhang, Wanquan Liu
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Unilateral Orthogonal Nonnegative Matrix Factorization
SIAM Journal on Control and Optimization, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shang, Jun, Chen, Tongwen
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Quadratic nonnegative matrix factorization
Pattern Recognition, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
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Factor-Bounded Nonnegative Matrix Factorization
ACM Transactions on Knowledge Discovery from Data, 2021Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems.
Kai Liu +4 more
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Nonnegative Unimodal Matrix Factorization
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry.
Andersen Man Shun Ang +3 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, Pascual-Montano +4 more
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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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Large-Cone Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks and Learning Systems, 2016Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based interpretation and the effective multiplicative updating rule for searching local solutions. In this paper, we study the problem of how to obtain an attractive local solution for NMF, which not only fits the given training data well but also generalizes well on the ...
Tongliang, Liu +2 more
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Labelwalking nonnegative matrix factorization
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion.
Long Lan +4 more
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Weighted nonnegative matrix factorization
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete.
Yong-Deok Kim, Seungjin Choi
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