Clustering Analysis Based on Hyper-graph Regularized Non-Negative Tensor Train Decomposition [PDF]
Jisuanji gongcheng, 2023 Non-negative Tensor Train（NTT） decomposition，as an important tensor decomposition model，can preserve the internal structure information of data and is widely used in feature extraction and representation tasks of high-dimensional data.
DAI Haolei, HUANG Yonghui, ZHOU Guoxu
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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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Computing the extremal nonnegative solutions of the M-tensor equation with a nonnegative right side vector [PDF]
, 2022 We consider the tensor equation whose coefficient tensor is a nonsingular M-tensor and whose right side vector is nonnegative. Such a tensor equation may have a large number of nonnegative solutions.
Guo, Chun-Hua
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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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Tensors of nonnegative rank two
Linear Algebra and its Applications, 2015 A nonnegative tensor has nonnegative rank at most 2 if and only if it is supermodular and has flattening rank at most 2. We prove this result, then explore the semialgebraic geometry of the general Markov model on phylogenetic trees with binary states, and comment on possible extensions to tensors of higher rank.
Elizabeth S. Allman +3 more
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A proximal ANLS algorithm for nonnegative tensor factorization with a periodic enhanced line search [PDF]
, 1999 summary:The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method.
Bunker, Douglas +3 more
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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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A Generalized Hierarchical Nonnegative Tensor Decomposition
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 6 pages, 2 figues, 3 ...
Joshua Vendrow +2 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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