Publication detail

Piecewise-polynomial Signal Segmentation Using Convex Optimization

RAJMIC, P. NOVOSADOVÁ, M. DAŇKOVÁ, M.

Original Title

Piecewise-polynomial Signal Segmentation Using Convex Optimization

English Title

Piecewise-polynomial Signal Segmentation Using Convex Optimization

Type

journal article

Language

en

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.

English 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

Released

31.12.2017

Publisher

Institute of Information Theory and Automation of the ASCR

Location

Prague

Pages from

1131

Pages to

1149

Pages count

19

URL

BibTex


@article{BUT138857,
  author="Pavel {Rajmic} and Michaela {Novosadová} and Marie {Mangová}",
  title="Piecewise-polynomial Signal Segmentation Using Convex Optimization",
  annote="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.",
  address="Institute of Information Theory and Automation of the ASCR",
  chapter="138857",
  doi="10.14736/kyb-2017-6-1131",
  howpublished="online",
  institution="Institute of Information Theory and Automation of the ASCR",
  number="6",
  volume="53",
  year="2017",
  month="december",
  pages="1131--1149",
  publisher="Institute of Information Theory and Automation of the ASCR",
  type="journal article"
}