Detail publikace

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

NOVOSADOVÁ, M. RAJMIC, P.

Originální název

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

Anglický název

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

Jazyk

en

Originální abstrakt

We present a method for segmenting a one-dimensional piecewise polynomial signal corrupted by an additive noise. The method’s principal part is based on sparse modeling, and its formulation as a reweighted convex optimization problem is solved numerically by proximal splitting. The method solves a sequence of weighted `21-minimization problems, where the weights used for the next iteration are computed from the current solution.We perform experiments on simulated and real data and discuss the results.

Anglický abstrakt

We present a method for segmenting a one-dimensional piecewise polynomial signal corrupted by an additive noise. The method’s principal part is based on sparse modeling, and its formulation as a reweighted convex optimization problem is solved numerically by proximal splitting. The method solves a sequence of weighted `21-minimization problems, where the weights used for the next iteration are computed from the current solution.We perform experiments on simulated and real data and discuss the results.

Dokumenty

BibTex 


@inproceedings{BUT135481,
  author="Michaela {Novosadová} and Pavel {Rajmic}",
  title="Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization",
  annote="We present a method for segmenting a one-dimensional piecewise polynomial signal corrupted by an additive noise. The method’s principal part is based on sparse modeling, and its formulation as a reweighted convex optimization problem is solved numerically by proximal splitting. The method solves a sequence of weighted `21-minimization problems, where the weights used for the next iteration are computed from the current solution.We perform experiments on simulated and real data and discuss the results.",
  booktitle="Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP) 2017",
  chapter="135481",
  doi="10.1109/TSP.2017.8076092",
  howpublished="electronic, physical medium",
  year="2017",
  month="july",
  pages="769--774",
  type="conference paper"
}