Detail publikačního výsledku

Piecewise-polynomial curve fitting using group sparsity

NOVOSADOVÁ, M.; RAJMIC, P.

Originální název

Piecewise-polynomial curve fitting using group sparsity

Anglický název

Piecewise-polynomial curve fitting using group sparsity

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

We present a method for segmenting onedimensional signal, whose segments are modeled as polynomials, and which is corrupted by an additive noise. The method is based on sparse modeling and its formulation as a convex optimization problem is solved by proximal algorithms. We perform experiments on simulated data, discuss the results and suggest future directions that could lead to even better results.

Anglický abstrakt

We present a method for segmenting onedimensional signal, whose segments are modeled as polynomials, and which is corrupted by an additive noise. The method is based on sparse modeling and its formulation as a convex optimization problem is solved by proximal algorithms. We perform experiments on simulated data, discuss the results and suggest future directions that could lead to even better results.

Klíčová slova

Signal segmentation; sparsity; convex optimization

Klíčová slova v angličtině

Signal segmentation; sparsity; convex optimization

Autoři

NOVOSADOVÁ, M.; RAJMIC, P.

Rok RIV

2017

Vydáno

18.10.2016

Nakladatel

EDAS Conference Services

Místo

Lisbon

ISBN

978-1-4673-8817-7

Kniha

Proceedings of the 8th International Congress on Ultra Modern Telecommunications and Control Systems

Strany od

320

Strany do

325

Strany počet

6

URL

https://ieeexplore.ieee.org/document/7765379

BibTex

@inproceedings{BUT127475,
  author="Michaela {Novosadová} and Pavel {Rajmic}",
  title="Piecewise-polynomial curve fitting using group sparsity",
  booktitle="Proceedings of the 8th International Congress on Ultra Modern Telecommunications and Control Systems",
  year="2016",
  pages="320--325",
  publisher="EDAS Conference Services",
  address="Lisbon",
  doi="10.1109/ICUMT.2016.7765379",
  isbn="978-1-4673-8817-7",
  url="https://ieeexplore.ieee.org/document/7765379"
}