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

Citation

Cuyt A, Knaepkens F & Lee W (2018) From exponential analysis to Padé approximation and tensor decomposition, in one and more dimensions. In: Gerdt V, Koepf W, Seiler W & Vorozhtsov E (eds.) Computer Algebra in Scientific Computing. CASC 2018. Lecture Notes in Computer Science (LNCS), 11077. Computer Algebra in Scientific Computing, Lille, France, 17.09.2018-21.09.2018. Cham, Switzerland: Springer International Publishing, pp. 116-130. https://doi.org/10.1007/978-3-319-99639-4_8

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.

Keywords
Exponential analysis; Parametric method; Multivariate Padé approximation; Tensor decomposition