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Tensor Completion via A Generalized Transformed Tensor T-Product Decomposition Without t-SVD
Journal of Scientific Computing, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongjin He, He Hongjin
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Decompositions of third‐order tensors: HOSVD, T‐SVD, and Beyond
Numerical Linear Algebra with Applications, 2020SummaryThe higher order singular value decomposition, which is regarded as a generalization of the matrix singular value decomposition (SVD), has a long history and is well established, while the T‐SVD is relatively new and lacks systematic analysis. Because of the unusual tensor‐tensor product that the T‐SVD is based on, the form of the T‐SVD may be ...
Chao Zeng 0003, Michael K. Ng 0001
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SVD-Based Tensor-Completion Technique for Background Initialization
IEEE Transactions on Image Processing, 2018Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices from ...
Ibrahim Kajo +3 more
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SVD-based algorithms for tensor wheel decomposition
Advances in Computational MathematicszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hanyu Li, Li Hanyu
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Online Tensor Completion and Free Submodule Tracking With The T-SVD
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020We propose a new online algorithm, called TOUCAN, for the tensor completion problem of imputing missing entries of a low tubal-rank tensor using the tensor-tensor product (t- product) and tensor singular value decomposition (t-SVD) algebraic framework.
Kyle Gilman, Laura Balzano
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Dynamic MRI Reconstruction Using Tensor-SVD
2018 14th IEEE International Conference on Signal Processing (ICSP), 2018In this paper we propose to reconstruct dynamic magnetic resonance images from highly sparse sampling k-t space data by enhancing the low rankness and sparsity simultaneously. We introduce Tensor Singular Value Decomposition (t-SVD) instead of matrix SVD to maintain the structure of dynamic MR images.
Jianhang Ai +3 more
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Sequential Unfolding SVD for Tensors With Applications in Array Signal Processing
IEEE Transactions on Signal Processing, 2009This paper contributes to the field of higher order (N > 2) tensor decompositions in signal processing. A novel PARATREE tensor model is introduced, accompanied with sequential unfolding SVD (SUSVD) algorithm. SUSVD, as the name indicates, applies a matrix singular value decomposition sequentially on the unfolded tensor reshaped from the right hand ...
Jussi Salmi +2 more
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Sequential unfolding SVD for low rank orthogonal tensor approximation
2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008This paper contributes to the field of N-way (N ges 3) tensor decompositions, which are increasingly popular in various signal processing applications. A novel PARATREE decomposition structure is introduced, accompanied with sequential unfolding SVD (SUSVD) algorithm.
Jussi Salmi +2 more
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Multi-view Spectral Clustering via Tensor-SVD Decomposition
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017Multi-view clustering has attracted considerable attention in recent years, some related approaches always use matrices to represent views, and model by capturing two dimensional structure among views. The critical deficiency of these work is ignoring the space structure information of all views, which results in the mediocre performance of clustering.
Yan Zhang +5 more
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Low Rank Tensor STAP filter based on multilinear SVD
2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012Space Time Adaptive Processing (STAP) is a two-dimensional adaptive filtering technique which uses jointly temporal and spatial dimensions to suppress disturbance and to improve target detection. Disturbance contains both the clutter arriving from signal backscattering of the ground and the thermal noise resulting from the sensors noise.
Boizard, Mélanie +3 more
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