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IEEE Transactions on Network Science and Engineering, 2021
Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle
Xin Luo+4 more
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Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle
Xin Luo+4 more
semanticscholar +1 more source
Non-negative wavelet matrix factorization-based bearing fault intelligent classification method
Measurement science and technology, 2023There are more and more bearing fault types under considering the fault location and degree, and the corresponding fault classification task is becoming increasingly heavy.
Zhilin Dong, Dezun Zhao, Lingli Cui
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Robust non-negative matrix factorization [PDF]
Non-negative matrix factorization (NMF) is a recently popularized technique for learning parts-based, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted.
Zhengguang Chen+3 more
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Uniqueness of Non-Negative Matrix Factorization [PDF]
In this paper, two new properties of stochastic vectors are introduced and a strong uniqueness theorem on non-negative matrix factorizations (NMF) is introduced. It is described how the theorem can be applied to two of the common application areas of NMF, namely music analysis and probabilistic latent semantic analysis. Additionally, the theorem can be
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, 1994
A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m.
P. Paatero, U. Tapper
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A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m.
P. Paatero, U. Tapper
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Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering
IEEE Transactions on Cybernetics, 2020Multiview data processing has attracted sustained attention as it can provide more information for clustering. To integrate this information, one often utilizes the non-negative matrix factorization (NMF) scheme which can reduce the data from different ...
Zuyuan Yang+4 more
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In Analysis, 2021
Determination of the molecular composition of the skin is crucial for numerous tasks in medicine, pharmacology, dermatology and cosmetology. Confocal Raman microspectroscopy is a sensitive method for the evaluation of molecular depth profiles in the skin
B. P. Yakimov+6 more
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Determination of the molecular composition of the skin is crucial for numerous tasks in medicine, pharmacology, dermatology and cosmetology. Confocal Raman microspectroscopy is a sensitive method for the evaluation of molecular depth profiles in the skin
B. P. Yakimov+6 more
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Semi-Supervised Non-Negative Matrix Factorization With Dissimilarity and Similarity Regularization
IEEE Transactions on Neural Networks and Learning Systems, 2020In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model by means of elegantly modeling the label information. The proposed model is capable of generating discriminable low-dimensional representations to improve ...
Yuheng Jia+3 more
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Dropout non-negative matrix factorization
Knowledge and Information Systems, 2018Non-negative matrix factorization (NMF) has received lots of attention in research communities like document clustering, image analysis, and collaborative filtering. However, NMF-based approaches often suffer from overfitting and interdependent features which are caused by latent feature co-adaptation during the learning process.
Jie Liu+5 more
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Convex Non-Negative Matrix Factorization With Adaptive Graph for Unsupervised Feature Selection
IEEE Transactions on Cybernetics, 2020Unsupervised feature selection (UFS) aims to remove the redundant information and select the most representative feature subset from the original data, so it occupies a core position for high-dimensional data preprocessing.
Aihong Yuan+3 more
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