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Constrained Nonnegative Matrix Factorization for Image Representation
Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and ...
Thomas Huang, Deng Cai, Xuelong Li
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Robust Capped Norm Nonnegative Matrix Factorization
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015As an important matrix factorization model, Nonnegative Matrix Factorization (NMF) has been widely used in information retrieval and data mining research. Standard Nonnegative Matrix Factorization is known to use the Frobenius norm to calculate the residual, making it sensitive to noises and outliers.
Hongchang Gao +3 more
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Journal of Global Optimization, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rundong Du +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rundong Du +3 more
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Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation
2008We proposed a novel algorithm of supervised feature selection and adaptation for enhancing the classification accuracy of unsupervised Nonnegative Matrix Factorization (NMF) feature extraction algorithm. At first the algorithm extracts feature vectors for a given high dimensional data then reduce the feature dimension using mutual information based ...
Barman, Paresh Chandra, Lee, Soo-Young
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The Journal of Supercomputing, 2018
Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix.
Chaobo He +5 more
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Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix.
Chaobo He +5 more
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2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019
In this paper, the problem of electrodes shift is studied in low-density surface electromyographic (sEMG) based prosthetic control with the proposed Sliding Window Nonnegative Matrix Factorization (SW-NMF) algorithm. By artificially switching the electrode positions clockwise for π/8, the 8 channel sEMG signals of 10 gestures were recorded before and ...
Zhien Xian +4 more
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In this paper, the problem of electrodes shift is studied in low-density surface electromyographic (sEMG) based prosthetic control with the proposed Sliding Window Nonnegative Matrix Factorization (SW-NMF) algorithm. By artificially switching the electrode positions clockwise for π/8, the 8 channel sEMG signals of 10 gestures were recorded before and ...
Zhien Xian +4 more
openaire +2 more sources
SAR Images Clustering Based on Modified Nonlinear Orthogonal Nonnegative Matrix Factorization (NMF)
2023 31st International Conference on Electrical Engineering (ICEE), 2023Mahdi Jowkar Dehouei +2 more
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Generalized Separable Nonnegative Matrix Factorization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Nicolas Gillis, Junjun Pan
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Robust Manifold Nonnegative Matrix Factorization
ACM Transactions on Knowledge Discovery From Data, 2014Feiping Nie, Heng Huang, Chris H Q Ding
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SVD Based Initialization: A Head Start for Nonnegative Matrix Factorization
Pattern Recognition, 2008Efstratios Gallopoulos
exaly

