Results 201 to 210 of about 1,627,028 (236)
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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 0018 +4 more
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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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Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization
SIAM Journal on Matrix Analysis and Applications, 2018 © 2018 Society for Industrial and Applied Mathematics. In this paper, we study orthogonal nonnegative matrix factorization. We demonstrate the coefficient matrix can be sparse and low-rank in the orthogonal nonnegative matrix factorization.
Michael Ng
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Nonnegative matrix factorization: When data is not nonnegative
2014 7th International Conference on Biomedical Engineering and Informatics, 2014In this paper, we present a new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values. When a NMF problem is formulated as μ ≈μμ, we try to develop a new method that only allows μ to contain nonnegative values, but allows both μ and μ to have both nonnegative and negative values. In this
Siyuan Wu, Jim Wang
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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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Nonnegative Discriminant Matrix Factorization
IEEE Transactions on Circuits and Systems for Video Technology, 2017Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification.
Yuwu Lu +5 more
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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.
Jun Shang, Tongwen Chen
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Nonnegative Matrix Factorization With Regularizations
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2014Matrix factorization techniques have been frequently applied in many fields. Among them, nonnegative matrix factorization (NMF) has received considerable attention for it aims to find a parts-based, linear representations of nonnegative data. Recently, many researchers propose various manifold learning algorithms to enhance learning performance by ...
Weiya Ren, Guohui Li, Dan Tu, Li Jia
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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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