Detail publikace

Piecewise-polynomial Signal Segmentation Using Convex Optimization

Originální název

Piecewise-polynomial Signal Segmentation Using Convex Optimization

Anglický název

Piecewise-polynomial Signal Segmentation Using Convex Optimization

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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 in Web of Science"
}