Publication detail

Piecewise-polynomial signal segmentation using proximal splitting convex optimization methods

NOVOSADOVÁ, M. RAJMIC, P.

Original Title

Piecewise-polynomial signal segmentation using proximal splitting convex optimization methods

Type

presentation, poster

Language

English

Original Abstract

We show how the problem of segmenting noisy piecewise polynomial signal can be formulated as a convex optimization task. Because the number of model changes in signal is considered low in comparison to the overall number of data points, we rely on the concept of sparsity and its convex-relaxed counterpart, the l1-norm. We present an unconstrained, overparametrized optimization formulation whose solution can be used for detecting the breakpoints, and for robust data denoising, in consequence. The problem is solved numerically by iterative proximal splitting methods.

Keywords

Signal segmentation; sparsity; convex optimization

Authors

NOVOSADOVÁ, M.; RAJMIC, P.

Released

8. 6. 2016

Pages from

1

Pages to

14

Pages count

14

URL

http://www.utko.feec.vutbr.cz/~rajmic/talks/APMOD_2016-Novosadova.pdf

BibTex

@misc{BUT127474,
  author="Michaela {Novosadová} and Pavel {Rajmic}",
  title="Piecewise-polynomial signal segmentation using proximal splitting convex optimization methods",
  year="2016",
  pages="1--14",
  url="http://www.utko.feec.vutbr.cz/~rajmic/talks/APMOD_2016-Novosadova.pdf",
  note="presentation, poster"
}

Documents

APMOD_2016-Novosadova.pdf