Results 71 to 80 of about 1,665,186 (233)
Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation
IEEE AccessIn this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor.
Xixiang Chen +4 more
doaj +1 more source
Tensor decomposition and homotopy continuation [PDF]
, 2016 A computationally challenging classical elimination theory problem is to compute polynomials which vanish on the set of tensors of a given rank. By moving away from computing polynomials via elimination theory to computing pseudowitness sets via ...
Bernardi, Alessandra +3 more
core +4 more sources
Robust Tensor Decomposition for Heterogeneous Beamforming Under Imperfect Channel State Information
IEEE Open Journal of Signal Processing, 2023 We propose a new robust variation of the tensor decomposition known as the multi-linear generalized singular value decomposition (ML-GSVD), and demonstrate its effectiveness in the design of joint transmit (TX) and receive (RX) beamforming (BF) for both ...
Kengo Ando +2 more
doaj +1 more source
A condition number for the tensor rank decomposition [PDF]
, 2016 The tensor rank decomposition problem consists of recovering the unique set of parameters representing a robustly identifiable low-rank tensor when the coordinate representation of the tensor is presented as input.
Vannieuwenhoven, Nick
core +2 more sources
Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the
Le Sun +5 more
semanticscholar +1 more source
Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent
IEEE AccessThe low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
doaj +1 more source
Tensor decompositions for learning latent variable models [PDF]
, 2014 This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which exploits a certain
Anandkumar, Anima +4 more
core +5 more sources
Tensor Decompositions for Modeling Inverse Dynamics
, 2017 Modeling inverse dynamics is crucial for accurate feedforward robot control. The model computes the necessary joint torques, to perform a desired movement.
Baier, Stephan, Tresp, Volker
core +1 more source
Remote Sensing, 2020 The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area.
Yanan You, Rui Wang, Wenli Zhou
doaj +1 more source
Spectrum Cartography via Coupled Block-Term Tensor Decomposition [PDF]
IEEE Transactions on Signal Processing, 2019 Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)—from limited samples taken sparsely over the region.
Guoyong Zhang +4 more
semanticscholar +1 more source