Publication detail

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

NOVOSADOVÁ, M. RAJMIC, P.

Original Title

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

English Title

Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

proximal splitting algorithm; reweighted convex optimization; signal segmentation; signal smoothing; sparsity

Released

07.07.2017

Location

Barcelona

ISBN

978-1-5090-3981-4

Book

Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP) 2017

Pages from

769

Pages to

774

Pages count

6

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"
}