Results 21 to 30 of about 4,031 (246)
Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering
International Journal of Computational Intelligence Systems, 2020 Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion.
Xiangguang Dai +3 more
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Generalized Separable Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 31 pages, 12 figures, 4 tables. We have added discussions about the identifiability of the model, we have modified the first synthetic experiment, we have clarified some aspects of the ...
Pan, Junjun, Gillis, Nicolas
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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]
ITM Web of Conferences, 2022 —What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
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Quantized nonnegative matrix factorization [PDF]
2014 19th International Conference on Digital Signal Processing, 2014 Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of
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Continuous Semi-Supervised Nonnegative Matrix Factorization
Algorithms, 2023 Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom +4 more
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Uncovering community structures with initialized Bayesian nonnegative matrix factorization. [PDF]
PLoS ONE, 2014 Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks.
Xianchao Tang +3 more
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Boolean Matrix Factorization via Nonnegative Auxiliary Optimization
IEEE Access, 2021 A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix whose Boolean structure is ...
Duc P. Truong +3 more
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Nonnegative Matrix Factorization Requires Irrationality [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$. A longstanding open question, posed by Cohen and Rothblum in 1993, is whether a rational matrix $M$ always has an NMF of minimal inner ...
Chistikov, D +4 more
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Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
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Multi-Component Nonnegative Matrix Factorization [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand ...
Wang, Jing +8 more
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