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1. Full Text Channel Estimation for Millimeter-Wave Multiuser MIMO Systems via PARAFAC Decomposition
by Zhou Zhou and Jun Fang and Linxiao Yang and Hongbin Li and Zhi Chen and Shaoqian Li
IEEE transactions on wireless communications, ISSN 1536-1276, 11/2016, Volume 15, Issue 11, pp. 7501 - 7516
Training | Radio frequency | Tensile stress | Array signal processing | compressed sensing | Mm-Wave systems | Channel estimation | CANDECOMP/PARAFAC (CP) decomposition | MIMO | Antenna arrays | CANDECOMP/ PARAFAC (CP) decomposition | channel estimation | Engineering, Electrical & Electronic | Engineering | Telecommunications | Technology | Science & Technology | Beamforming | Channels | Millimeter waves | Uniqueness
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2. Full Text Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising
by Xue, Jize and Zhao, Yongqiang and Liao, Wenzhi and Chan, Jonathan Cheung-Wai
IEEE transactions on geoscience and remote sensing, ISSN 0196-2892, 07/2019, Volume 57, Issue 7, pp. 5174 - 5189
Correlation | nonlocal low-rank regularization (LR) | Noise reduction | Estimation | hyperspectral image (HSI) denoising | rank automatic determination | CANDECOMP/PARAFAC (CP) tensor decomposition (CPTD) | rank estimation bias | Hyperspectral imaging | Engineering | Physical Sciences | Technology | Engineering, Electrical & Electronic | Remote Sensing | Imaging Science & Photographic Technology | Science & Technology | Geochemistry & Geophysics
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3. Full Text Canonical polyadic decomposition of third-order tensors: Reduction to generalized eigenvalue decomposition
by Domanov, Ignat and De Lathauwer, Lieven
SIAM journal on matrix analysis and applications, ISSN 0895-4798, 2014, Volume 35, Issue 2, pp. 636 - 660
Canonical polyadic decomposition | Mixed discriminant | Compound matrix | Tensor | Candecomp/Parafac decomposition | Permanent | Khatri-Rao product | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Tensors | Algebra | Algorithms | Computation | Mathematical analysis | Compounding | Decomposition | Compounds | Mathematics - Spectral Theory
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4. Full Text A practical randomized CP tensor decomposition
by Battaglino, Casey and Ballard, Grey and Kolda, Tamara G
SIAM journal on matrix analysis and applications, ISSN 0895-4798, 2018, Volume 39, Issue 2, pp. 876 - 901
CANDECOMP/PARAFAC (CP) | Randomized algorithms | Randomized least squares | Multilinear algebra | Canonical polyadic tensor decomposition | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Computer Science - Numerical Analysis
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5. Full Text Tensor rank learning in CP decomposition via convolutional neural network
by Zhou, Mingyi and Liu, Yipeng and Long, Zhen and Chen, Longxi and Zhu, Ce
Signal processing. Image communication, ISSN 0923-5965, 04/2019, Volume 73, pp. 12 - 21
Deep learning | CANDECOMP/PARAFAC decomposition | Tensor rank estimation | Convolutional neural network | Low rank tensor approximation | Engineering, Electrical & Electronic | Engineering | Technology | Science & Technology | Rankings | Information management | Neural networks | Analysis | Data analysis | Tensors | Decomposition | Mathematical analysis | Artificial neural networks
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6. Full Text The probabilistic tensor decomposition toolbox
by Hinrich, Jesper L and Madsen, Kristoffer H and Mørup, Morten
Machine Learning: Science and Technology, ISSN 2632-2153, 6/2020, Volume 1, Issue 2, p. 25011
Tucker | tensor train | candecomp/PARAFAC | Bayesian inference | multi-way modelling | tensor decomposition
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7. Full Text Generic uniqueness conditions for the canonical polyadic decomposition and indscal
by Domanov, Ignat and De Lathauwer, Lieven
SIAM journal on matrix analysis and applications, ISSN 0895-4798, 2015, Volume 36, Issue 4, pp. 1567 - 1589
Canonical polyadic decomposition | CANDECOMP/PARAFAC decomposition | Third-order tensor | Algebraic geometry | Uniqueness | INDSCAL | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Mathematics - Algebraic Geometry
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8. Full Text Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
by Shen, Yanning and Baingana, Brian and Giannakis, Georgios B
IEEE transactions on signal processing, ISSN 1053-587X, 07/2017, Volume 65, Issue 14, pp. 3675 - 3687
Tensile stress | Correlation | Network topology | Numerical analysis | CANDECOMP/PARAFAC (CP) decomposition | Real-time systems | Topology | network topology inference | Mathematical model | Structural equation models | Network topology inference | CANDECOMP/ PARAFAC (CP) decomposition | Engineering, Electrical & Electronic | Engineering | Technology | Science & Technology | Usage | Graph theory | Structural equation modeling | Statistics - Machine Learning
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9. Full Text Non-linear model-order reduction based on tensor decomposition and matrix product
by Li, Zhen and Jiang, Yaolin and Xu, Kangli
IET control theory & applications, ISSN 1751-8644, 11/2018, Volume 12, Issue 16, pp. 2253 - 2262
Research Article | matrix product | expansion coefficients | nonlinear model‐order reduction | tensor calculus | tensors | orthogonal decomposition method | matrix algebra | nonlinear input–output systems | CANDECOMP/PARAFAC decomposition | MOR method | reduced order systems | quadratic term coefficient | quadratic‐bilinear systems | transformational matrix | Tucker decomposition | reduced QB system | tensor decomposition | Engineering, Electrical & Electronic | Engineering | Automation & Control Systems | Technology | Instruments & Instrumentation | Science & Technology
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10. Full Text Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition
by Wang, Deqing and Zhu, Yongjie and Ristaniemi, Tapani and Cong, Fengyu
Journal of neuroscience methods, ISSN 0165-0270, 10/2018, Volume 308, pp. 240 - 247
Event-related potential | Component number selection | CANDECOMP/PARAFAC | Multi-mode features | Nonnegative tensor decomposition | Biochemistry & Molecular Biology | Neurosciences | Neurosciences & Neurology | Life Sciences & Biomedicine | Biochemical Research Methods | Science & Technology | Analysis | Algorithms | Index Medicus
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11. Full Text Canonical polyadic decomposition of third-order tensors: Relaxed uniqueness conditions and algebraic algorithm
by Domanov, Ignat and De Lathauwer, Lieven
Linear algebra and its applications, ISSN 0024-3795, 01/2017, Volume 513, pp. 342 - 375
Canonical polyadic decomposition | CANDECOMP/PARAFAC decomposition | Tensor | Uniqueness of CPD | Eigenvalue decomposition | Uni-mode uniqueness | CP decomposition | Singular value decomposition | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Electrical engineering | Algebra | Algorithms | Tensors | Matrix | Computer simulation | Mathematical analysis | Linear algebra | Uniqueness | Decomposition | Compounds
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12. Full Text Computing the gradient in optimization algorithms for the CP decomposition in constant memory through tensor blocking
by Vannieuwenhoven, Nick and Meerbergen, Karl and Vandebril, Raf
SIAM Journal on Scientific Computing, ISSN 1064-8275, 2015, Volume 37, Issue 3, pp. C415 - C438
CP gradient | Tensor rank decomposition | CANDECOMP/PARAFAC | CP decomposition | Implicit block unfolding | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology
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13. Full Text Low rank tensor completion for multiway visual data
by Long, Zhen and Liu, Yipeng and Chen, Longxi and Zhu, Ce
Signal processing, ISSN 0165-1684, 02/2019, Volume 155, pp. 301 - 316
Color image and video completion | Low-rank tensor approximation | CANDECOMP/PARAFAC Decomposition | Tucker decomposition | Tensor singular value decomposition | Tensor network | Engineering, Electrical & Electronic | Engineering | Technology | Science & Technology | Analysis | Algorithms
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14. Full Text Low CP Rank and Tucker Rank Tensor Completion for Estimating Missing Components in Image Data
by Liu, Yipeng and Long, Zhen and Huang, Huyan and Zhu, Ce
IEEE transactions on circuits and systems for video technology, ISSN 1051-8215, 04/2020, Volume 30, Issue 4, pp. 944 - 954
image recovery | low rank tensor completion | Minimization methods | CANDECOMP/PARAFAC decomposition | Data structures | Convex functions | Numerical models | Matrix decomposition | Tucker decomposition | Optimization | Engineering, Electrical & Electronic | Engineering | Technology | Science & Technology
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