Results 161 to 170 of about 223,331 (198)
Transcriptional analyses identify pericyte-centered signaling programs altered by sex and brain region in Alzheimer's Disease. [PDF]
Commun BiolEl-Ghazawi K +7 more
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Bayesian multi-tensor factorization [PDF]
Machine Learning, 2016 We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix-tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as unsupervised multi-view learning ...
Suleiman A Khan +2 more
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Tensor Factorization for Low-Rank Tensor Completion
IEEE Transactions on Image Processing, 2018 Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its ...
Pan Zhou, Canyi Lu, Lin, Zhouchen
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Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion
Neurocomputing, 2021 Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD).
Rita Cucchiara, Shiqiang Du, Yuqing Shi
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Pattern Recognition Letters, 2001
Summary: A novel fixed point algorithm for Positive Tensor Factorization (PTF) is introduced. The update rules efficiently minimize the reconstruction error of a positive tensor over positive factors. Tensors of arbitrary order can be factorized, which extends earlier results in the literature.
Max Welling, Markus Weber
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Summary: A novel fixed point algorithm for Positive Tensor Factorization (PTF) is introduced. The update rules efficiently minimize the reconstruction error of a positive tensor over positive factors. Tensors of arbitrary order can be factorized, which extends earlier results in the literature.
Max Welling, Markus Weber
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Fast Nonnegative Tensor Factorizations with Tensor Train Model
Lobachevskii Journal of Mathematics, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shcherbakova, E. M., Tyrtyshnikov, E. E.
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Bayesian Factorizations of Big Sparse Tensors [PDF]
Journal of the American Statistical Association, 2015 It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited consideration of extensions to the tensor case in statistics. The most common low rank tensor factorization relies on parallel factor analysis (PARAFAC), which expresses a rank
Anirban Bhattacharya +2 more
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Coupled graph tensor factorization
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016Factorization of a single matrix or tensor has been used widely to reveal interpretable factors or predict missing data. However, in many cases side information may be available, such as social network activities and user demographic data together with Netflix data.
Ahmed S. Zamzam +2 more
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A faster tensor robust PCA via tensor factorization
International Journal of Machine Learning and Cybernetics, 2020Many kinds of real-world multi-way signal, like color images, videos, etc., are represented in tensor form and may often be corrupted by outliers. To recover an unknown signal tensor corrupted by outliers, tensor robust principal component analysis (TRPCA) serves as a robust tensorial modification of the fundamental PCA.
Andong Wang +2 more
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