Results 211 to 220 of about 14,760 (255)

Multiobjective Sparse Non-Negative Matrix Factorization

IEEE Transactions on Cybernetics, 2019
Non-negative matrix factorization (NMF) is becoming increasingly popular in many research fields due to its particular properties of semantic interpretability and part-based representation. Sparseness constraints are usually imposed on the NMF problems in order to achieve potential features and sparse representation.
Maoguo Gong, Xiangming Jiang, Hao Li, Kay Chen Tan   +3 more
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Semisupervised Adaptive Symmetric Non-Negative Matrix Factorization

IEEE Transactions on Cybernetics, 2021
As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can generate the clustering result without additional post-processing, by decomposing a similarity matrix into the product of a clustering indicator matrix and its transpose.
Yuheng, Jia, Hui, Liu, Junhui, Hou, Sam, Kwong   +3 more
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Farness preserving Non-negative matrix factorization

2014 IEEE International Conference on Image Processing (ICIP), 2014
Dramatic growth in the volume of data made a compact and informative representation of the data highly demanded in computer vision, information retrieval, and pattern recognition. Non-negative Matrix Factorization (NMF) is used widely to provide parts-based representations by factorizing the data matrix into non-negative matrix factors.
Babaee, Mohammadreza, Bahmanyar, Reza, Rigoll, Gerhard, Datcu, Mihai   +3 more
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Robust non-negative matrix factorization

Frontiers of Electrical and Electronic Engineering in China, 2011
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.
Lijun Zhang, Zhengguang Chen, Miao Zheng, Xiaofei He   +3 more
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Dropout non-negative matrix factorization

Knowledge and Information Systems, 2018
Non-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.
Zhicheng He, Jie Liu, Caihua Liu, Yuan Wang, Airu Yin, Yalou Huang   +5 more
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Autofluorescence Removal by Non-Negative Matrix Factorization

IEEE Transactions on Image Processing, 2011
This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is
Franco, Woolfe, Michael, Gerdes, Musodiq, Bello, Xiaodong, Tao, Ali, Can   +4 more
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Non-negative matrix factorization

2017
Non-negative matrix factorization - NMF is a Linear Dimensionality Reduction method, which approximates a high dimensional non-negative data matrix by a multiplica- tion of two low-ranked matrices that preserves the non-negativity of the data. This property has proven to be beneficial as it allows for the approximated data to be interpreted in the same
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Novel Algorithm for Non-Negative Matrix Factorization

New Mathematics and Natural Computation, 2015
Non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with non-negative constraints. This problem attracts much attention from researchers for theoretical reasons and for potential applications.
Tran Dang Hien, Do Van Tuan, Pham Van At, Le Hung Son   +3 more
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Email Surveillance Using Non-negative Matrix Factorization

Computational and Mathematical Organization Theory, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Berry, Michael W., Browne, Murray
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Rank-Adaptive Non-Negative Matrix Factorization

Cognitive Computation, 2018
Dimension reduction is a challenge task in data processing, especially in high-dimensional data processing area. Non-negative matrix factorization (NMF), as a classical dimension reduction method, has a contribution to the parts-based representation for the characteristics of non-negative constraints in the NMF algorithm.
Dong Shan, Xinzheng Xu, Tianming Liang, Shifei Ding   +3 more
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