TY - JOUR
T1 - A locally convergent Jacobi iteration for the tensor singular value problem
AU - Shekhawat, Hanumant Singh
AU - Weiland, Siep
PY - 2018/7/1
Y1 - 2018/7/1
N2 - Multi-linear functionals or tensors are useful in study and analysis multi-dimensional signal and system. Tensor approximation, which has various applications in signal processing and system theory, can be achieved by generalizing the notion of singular values and singular vectors of matrices to tensor. In this paper, we showed local convergence of a parallelizable numerical method (based on the Jacobi iteration) for obtaining the singular values and singular vectors of a tensor.
AB - Multi-linear functionals or tensors are useful in study and analysis multi-dimensional signal and system. Tensor approximation, which has various applications in signal processing and system theory, can be achieved by generalizing the notion of singular values and singular vectors of matrices to tensor. In this paper, we showed local convergence of a parallelizable numerical method (based on the Jacobi iteration) for obtaining the singular values and singular vectors of a tensor.
KW - Jacobi iteration
KW - Singular value decompositions
KW - Tensor decompositions
UR - http://www.scopus.com/inward/record.url?scp=85016442894&partnerID=8YFLogxK
U2 - 10.1007/s11045-017-0485-9
DO - 10.1007/s11045-017-0485-9
M3 - Article
AN - SCOPUS:85016442894
SN - 0923-6082
VL - 29
SP - 1075
EP - 1094
JO - Multidimensional Systems and Signal Processing
JF - Multidimensional Systems and Signal Processing
IS - 3
ER -