Title




From exponential analysis to Padé approximation and Tensor decomposition, in one and more dimensions


Author






Abstract




Exponential analysis in signal processing is essentially what is known as sparse interpolation in computer algebra. We show how exponential analysis from regularly spaced samples is reformulated as Padé approximation from approximation theory and tensor decomposition from multilinear algebra. The univariate situation is briefly recalled and discussed in Sect. 1. The new connections from approximation theory and tensor decomposition to the multivariate generalization are the subject of Sect. 2. These connections immediately allow for some generalization of the sampling scheme, not covered by the current multivariate theory. An interesting computational illustration of the above in blind source separation is presented in Sect. 3. 


Language




English


Source (journal)




Lecture notes in computer science.  Berlin, 1973, currens


Source (book)




CASC 2018 : Computer Algebra in Scientific Computing
Proceedings 20th International Workshop, CASC 2018, September 1721, 2018, Lille, France


Source (series)




Theoretical computer science and general issues ; 11077


Publication




Cham
:
Springer
,
2018


ISBN




9783319996387


Volume/pages




(2018)
, p. 116130


ISI




000511448200008


Full text (Publisher's DOI)






Full text (open access)






Full text (publisher's version  intranet only)





