Results 11 to 20 of about 38,023 (256)
Computing non-negative tensor factorizations [PDF]
Optimization Methods and Software, 2008 Non-negative tensor factorization (NTF) is a technique for computing a parts-based representation of high-dimensional data. NTF excels at exposing latent structures in datasets, and at finding good low-rank approximations to the data. We describe an approach for computing the NTF of a dataset that relies only on iterative linear-algebra techniques and ...
Michael P. Friedlander, Kathrin Hatz
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Sparse non-negative tensor factorization using columnwise coordinate descent [PDF]
Pattern Recognition, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Ji +3 more
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Applied Sciences, 2019 The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging.
Lin Liang +4 more
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Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games
Information, 2018 Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online ...
Anna Sapienza +2 more
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Non-negative tensor factorization for vibration-based local damage detection
Mechanical Systems and Signal Processing, 2023 In this study, a novel non-negative tensor factorization (NTF)-based method for vibration-based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-frequency method is frequently used as a primary decomposition technique.
Mateusz Gabor +4 more
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Alzheimer’s Disease Recognition Applying Non-Negative Matrix Factorization Characteristics from Brain Magnetic Resonance Images (MRI) [PDF]
E3S Web of Conferences, 2023 To more accurately depict Alzheimer’s disease (AD) and projecting clinical outcomes while taking into account advancements in clinical imaging and substantial learning, several experts are gradually using ConvNet (CNNs) to remove deep intensity features ...
Reddy G. Vijendar +4 more
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Non-negative Multiple Tensor Factorization [PDF]
2013 IEEE 13th International Conference on Data Mining, 2013 Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors.
Koh Takeuchi +4 more
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Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition
Remote Sensing, 2021 Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest.
Marzieh Zare +3 more
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Non-negative Tensor Factorization for Speech Enhancement [PDF]
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications, 2016 This paper proposes an algorithm for speech enhancement by non-negative tensor factorisation. We group adjacent time-frequency matrices in the spectrograms together to form a tensor as a basic input in our algorithm. The non-negative tensor factorisation is followed to perform sound source separation between speeches and noises.
Mengnan Shi, Weiqiang Zhang, Liang He
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