Blind Separation of Exponential Polynomials and the Decomposition of a Tensor in Rank-$(L_r,L_r,1)$ Terms
From MaRDI portal
Publication:3225546
DOI10.1137/100805510zbMath1239.15016OpenAlexW2017948584MaRDI QIDQ3225546
Publication date: 21 March 2012
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/100805510
singular value decompositionnumerical experimentssignal processingexponential polynomialsmultilinear algebrahigher-order tensorcanonical polyadic decompositionblind signal separationblock term decomposition
Numerical solutions to overdetermined systems, pseudoinverses (65F20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Eigenvalues, singular values, and eigenvectors (15A18) Multilinear algebra, tensor calculus (15A69)
Related Items
$(L_r,L_r,1)$-Decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials ⋮ Nonlinear algebra and applications ⋮ SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction ⋮ On Uniqueness and Computation of the Decomposition of a Tensor into Multilinear Rank-$(1,L_r,L_r)$ Terms ⋮ Exploiting Efficient Representations in Large-Scale Tensor Decompositions ⋮ On partial and generic uniqueness of block term tensor decompositions ⋮ Fast Hankel tensor–vector product and its application to exponential data fitting ⋮ Exact line and plane search for tensor optimization ⋮ On identifiability of higher order block term tensor decompositions of rank Lr⊗ rank-1 ⋮ From Computation to Comparison of Tensor Decompositions ⋮ Systems of Polynomial Equations, Higher-Order Tensor Decompositions, and Multidimensional Harmonic Retrieval: A Unifying Framework. Part II: The Block Term Decomposition ⋮ Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- $(L_{r,n},L_{r,n},1)$ Terms---Part II: Algorithms ⋮ Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-$(L_r,n,L_r,n,1)$ Terms---Part I: Uniqueness ⋮ Tensor factorisation for narrowband single channel source decomposition
