Parallel factor analysis in sensor array processing

By N. Sidiropoulos, Rasmus Bro, Georgios B. Giannakis. IEEE Transactions on Signal Processing, 2000

665 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d3d7e7eefe26f8102d1efff3bb3e32cafee5cd61

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In Aerospace Engineering

In Computational Mechanics

In Signal Processing

Citations in the corpus

Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, methodology): “In parallel, CPD was successfully adopted as a tool for sensor array processing and deterministic signal separation in wireless communication [17, 18].”
Tensor Decompositions and Applications (2009, background): “Sidiropoulos, Bro, and Giannakis [196] considered the application of CP to sensor array processing.”
On uniqueness conditions for Candecomp/Parafac and Indscal with full column rank in one mode (2009, neutral): “research; see Sidiropoulos, Giannakis and Bro [14] and Sidiropoulos, Bro and Giannakis [ 15 ].”
Higher-Order SVD-Based Subspace Estimation to Improve the Parameter Estimation Accuracy in Multidimensional Harmonic Retrieval Problems (2008, methodology): “This “Tucker3 compression” is used to speed up the iterative least squares fitting procedure of the PARAFAC model (without the Vandermonde structure that is specific to the harmonic retrieval problem), and thereby avoids a brute force implementation of ALS in the raw data space.”
On the uniqueness of multilinear decomposition of N‐way arrays (2000, background): “Lemma 1 is due to Sidiropoulos and Liu (submitted manuscript) which builds on an earlier result by Sidiropoulos et al.[ 10 ] on the rank of the Khatri‐Rao product.”
Tensors : A brief introduction (2014, neutral): “A quite interesting class of approaches consists of exploiting an additional diversity [74].”
Generic and typical ranks of multi-way arrays (2009, methodology): “Besides Chemometrics, CP has found important applications in signal processing, especially in Independent Component Analysis [16] [17] and in multi-user access in wireless communications [18] [19] [20].”
How hard is the tensor rank (2016, background): “Various recent publications, including [9, 23, 30, 37], explain the relevance of symmetric decompositions for problems in pure mathematics and engineering.”
Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme (2011, background): “Fields of application of higher-order tensors also include chemometrics [42], biomedical signal processing [31, 15, 16, 4], telecommunications [40, 41, 10], scientific computing [7, 6, 20, 35], and image processing [48].”
Decompositions of a Higher-Order Tensor in Block Terms - Part II: Definitions and Uniqueness (2008, background): “In wireless telecommunications, it provides powerful means for the exploitation of different types of diversity [49, 50, 18].”
A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization (2006, background): “In wireless telecommunications, it provides powerful means for the exploitation of different types of diversity [38, 39 ].”
On the best low multilinear rank approximation of higher-order tensors (2010, background): “It has applications in chemometrics [67], wireless communication [ 66 , 21], and can also be used for epileptic seizure onset localization [30, 29, 4], since only one of the rank-1 terms is related to the seizure activity.”
Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging (2013, background): “Third-order (and higher) tensors arise in a wide variety of application areas, including, but not limited to, chemometrics [24], psychometrics [13], and image and signal processing [5, 15, 23, 17, 8, 20, 29, 28, 30].”
On the typical rank of real binary forms (2009, background): “Motivation The decomposition of tensors into a minimal sum of rank-1 terms, sometimes referred to as the canonical polyadic (CP) decomposition, has raised a great interest in several quite different branches of engineering [3,8,13,22,25,28,29] as well as in mathematics [4,6,7,20,21,23,24].”
Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications (2020, methodology): “Recently, the PARAllel FACtor (PARAFAC) decomposition [36]–[40] has been considered as an efﬁcient method for estimating multiple large channel matrices in MIMO communication systems [41], [42].”