Results 31 to 40 of about 36,142 (194)
A new S-type upper bound for the largest singular value of nonnegative rectangular tensors
Journal of Inequalities and Applications, 2017 By breaking N = { 1 , 2 , … , n } $N=\{1,2,\ldots,n\}$ into disjoint subsets S and its complement, a new S-type upper bound for the largest singular value of nonnegative rectangular tensors is given and proved to be better than some existing ones ...
Jianxing Zhao, Caili Sang
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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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Nonnegative Structured Kruskal Tensor Regression
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023 Peer ...
Ollila Esa, Vorobyov Sergiy, Wang Xinjue
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Nonnegative Tensor Completion via Low-Rank Tucker Decomposition: Model and Algorithm
IEEE Access, 2019 We consider the problem of low-rank tensor decomposition of incomplete tensors that has applications in many data analysis problems, such as recommender systems, signal processing, machine learning, and image inpainting.
Bilian Chen +4 more
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Non-negative tensor factorization workflow for time series biomedical data
STAR Protocols, 2023 Summary: Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation.
Koki Tsuyuzaki +4 more
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Auditory Sparse Representation for Robust Speaker Recognition Based on Tensor Structure
EURASIP Journal on Audio, Speech, and Music Processing, 2008 This paper investigates the problem of speaker recognition in noisy conditions. A new approach called nonnegative tensor principal component analysis (NTPCA) with sparse constraint is proposed for speech feature extraction.
Liqing Zhang, Qiang Wu
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Results in Physics, 2017 For efficient exploitation of research reactors, it is important to discern neutron flux distribution inside the reactor with the best possible precision. For this reason, fission and ionization chambers are used to measure the neutron field.
Mounia Laassiri +2 more
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Journal of Inequalities and Applications, 2020 In this paper, we obtain a sharp upper bound on the spectral radius of a nonnegative k-uniform tensor and characterize when this bound is achieved. Furthermore, this result deduces the main result in [X. Duan and B.
Chuang Lv, Lihua You, Xiao-Dong Zhang
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Fast Decomposition of Large Nonnegative Tensors [PDF]
, 2015 International audienceIn Signal processing, tensor decompositions have gained in popularity this last decade. In the meantime, the volume of data to be processed has drastically increased. This calls for novel methods to handle Big Data tensors.
Cabral Farias, Rodrigo +2 more
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Discriminant Nonnegative Tensor Factorization Algorithms [PDF]
IEEE Transactions on Neural Networks, 2009 Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis, especially for object representation and recognition. NMF requires the object tensor (with valence more than one) to be vectorized. This procedure may result in information loss since the local object structure is lost due to vectorization. Recently, in order to
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