Results 271 to 280 of about 23,770 (308)
Nonnegative Tensor CP Decomposition of Hyperspectral Data
IEEE Transactions on Geoscience and Remote Sensing, 2016 International audienceNew hyperspectral missions will collect huge amounts of hyperspectral data. Besides, it is possible now to acquire time series and multiangular hyperspectral images.
MIGUEL A Veganzones +2 more
exaly +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Tensor Decompositions and Applications
SIAM Review, 2009Summary: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or \(N\)-way array. Decompositions of higher-order tensors (i.e., \(N\)-way arrays with \(N \geq 3\)) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra ...
Tamara G. Kolda, Brett W. Bader
openaire +2 more sources
Multiscale tensor decomposition
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016Large datasets usually contain redundant information and summarizing these datasets is important for better data interpretation. Higher-order data reduction is usually achieved through low-rank tensor approximation which assumes that the data lies near a linear subspace across each mode.
Alp Ozdemir +2 more
openaire +1 more source
Sensitivity in Tensor Decomposition
IEEE Signal Processing Letters, 2019Canonical polyadic (CP) tensor decomposition is an important task in many applications. Many times, the true tensor rank is not known, or noise is present, and in such situations, different existing CP decomposition algorithms provide very different results.
Petr Tichavský +2 more
openaire +1 more source
Provable Sparse Tensor Decomposition
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2017 Summary We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components.
Will Wei Sun, Junwei Lu, Guang Cheng
exaly +2 more sources
Tensor Decomposition Via Core Tensor Networks
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Tensor decomposition (TD) has shown promising performance in image completion and denoising. Existing methods always aim to decompose one tensor into latent factors or core tensors by optimizing a particular cost function based on a specific tensor model.
Jianfu Zhang 0003 +3 more
openaire +1 more source
SIAM Journal on Scientific Computing, 2011
A simple nonrecursive form of the tensor decomposition in $d$ dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices.
openaire +1 more source
A simple nonrecursive form of the tensor decomposition in $d$ dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices.
openaire +1 more source
Randomized Tensor Wheel Decomposition
SIAM Journal on Scientific ComputingzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyu Wang, Yajie Yu, Hanyu Li
openaire +1 more source
Facial Recognition Using Tensor-Tensor Decompositions
SIAM Journal on Imaging Sciences, 2013A tensor is a multidimensional array. First-order tensors and second-order tensors can be viewed as vectors and matrices, respectively. Tensors of higher order, with the ability to include more information, appear more frequently nowadays in image and signal processing, data mining, biomedical engineering, and so on.
Ning Hao +3 more
openaire +1 more source
Shape Constrained Tensor Decompositions
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019We propose a new low-rank tensor factorization where one mode is coded as a sparse linear combination of elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces r-rank approximations of data tensors via outer products of vectors in each ...
Bethany Lusch +2 more
openaire +1 more source