Blind Polynomial Regression

Blind Polynomial Regression

Natali, A. (TU Delft Signal Processing Systems)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2023

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering conventional regression approaches not applicable. In this paper, we formally state the (potentially partial) blind regression problem, illustrate some of its theoretical properties, and propose an algorithmic approach to solve it. As a case-study, we apply our methods to a jitter-correction problem and corroborate its performance.

polynomial regression
interpolation
Vandermonde
matrix factorization
MUSIC

http://resolver.tudelft.nl/uuid:2fe7652f-faf7-4fb2-bae0-b148e0c1c530

https://doi.org/10.1109/ICASSP49357.2023.10095361

IEEE, Piscataway

2023-11-05

978-1-7281-6328-4

Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, 2023-06-04 → 2023-06-10, Rhodes Island, Greece

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 A. Natali, G.J.T. Leus