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RAJMIC, P. NOVOSADOVÁ, M. DAŇKOVÁ, M.
Original Title
Piecewise-polynomial Signal Segmentation Using Convex Optimization
Type
journal article in Web of Science
Language
English
Original Abstract
A method is presented for segmenting one-dimensional signal whose independent segments are modeled as polynomials, and which is corrupted by additive noise. The method is based on sparse modeling, the main part is formulated as a convex optimization problem and is solved by a proximal splitting algorithm. We perform experiments on simulated and real data and show that the method is capable of reliably finding breakpoints in the signal, but requires careful tuning of the regularization parameters and internal parameters. Finally, potential extensions are discussed.
Keywords
Signal segmentation, Denoising, Sparsity, Piecewise-polynomial signal model, Convex optimization
Authors
RAJMIC, P.; NOVOSADOVÁ, M.; DAŇKOVÁ, M.
Released
31. 12. 2017
Publisher
Institute of Information Theory and Automation of the ASCR
Location
Prague
ISBN
0023-5954
Periodical
Kybernetika
Year of study
53
Number
6
State
Czech Republic
Pages from
1131
Pages to
1149
Pages count
19
URL
https://www.kybernetika.cz/content/2017/6/1131
BibTex
@article{BUT138857, author="Pavel {Rajmic} and Michaela {Novosadová} and Marie {Mangová}", title="Piecewise-polynomial Signal Segmentation Using Convex Optimization", journal="Kybernetika", year="2017", volume="53", number="6", pages="1131--1149", doi="10.14736/kyb-2017-6-1131", issn="0023-5954", url="https://www.kybernetika.cz/content/2017/6/1131" }