Abstract
In this paper we prove the existence of a time-frequency space that best approximates a given finite set of data. Here best approximation is in the least square sense, among all time-frequency spaces with no more than a prescribed number of generators. We provide a formula to construct the generators from the data and give the exact error of approximation. The setting is in the space of square integrable functions defined on a second countable LCA group and we use the Zak transform as the main tool.
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777822.
In addition, D. Barbieri and E. Hernández were supported by Grant MTM2016-76566-P (Ministerio de Ciencia, Innovación y Universidades, Spain). C. Cabrelli and U. Molter were supported by Grants UBACyT 20020170100430BA (University of Buenos Aires), PIP11220150100355 (CONICET) and PICT 2014-1480 (Ministerio de Ciencia, Tecnología e Innovación, Argentina).
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Appendix
Appendix
We give the proof of the following Lemma that has been used in Sect. 2 to prove part (2) of Theorem 2.
Lemma 1
Let \(\sigma : \mathbb H_1 \longrightarrow \mathbb H_2\) be an isometric isomorphism between the Hilbert spaces \(\mathbb H_1\) and \(\mathbb H_2\). For a measure space (X, dμ) the map \(Q_\sigma : L^2(X, \mathbb H_1) \longrightarrow L^2(X, \mathbb H_2)\) given by (Q σf)(x) = σ(f(x)) is also an isometric isomorphism.
Proof
Let f be a measurable vector function in \(L^2(X, \mathbb H_1)\), that is, for every \(y\in \mathbb H_1\) the scalar function \(x \longrightarrow \langle f(x) , y \rangle _{\mathbb H_1}\) is measurable. We must prove that Q σf is also a measurable vector function in \(L^2(X, \mathbb H_2)\). For \(z\in \mathbb H_2\) we have
Since σ ∗(z) = σ −1(z) is a general element of \(\mathbb H_1\), this shows that Q σf is measurable. Moreover, for \(f,g \in L^2(X, \mathbb H_1)\),
This shows that if \(f\in L^2(X, \mathbb H_1)\), \(Q_\sigma f \in L^2(X, \mathbb H_2)\) and that Q σ is an isometry.
Finally, it is easy to see that \(R: L^2(X, \mathbb H_2) \rightarrow L^2(X, \mathbb H_1)\) defined by Rg(x) = σ −1(g(x)) is the inverse and the adjoint of Q σ. Therefore, Q σ is onto. □
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Barbieri, D., Cabrelli, C., Hernández, E., Molter, U. (2020). Data Approximation with Time-Frequency Invariant Systems. In: Boggiatto, P., et al. Landscapes of Time-Frequency Analysis. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-56005-8_2
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DOI: https://doi.org/10.1007/978-3-030-56005-8_2
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