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Kybernetika 53 no. 6, 1131-1149, 2017

Piecewise-polynomial signal segmentation using convex optimization

Pavel Rajmic, Michaela Novosadová and Marie DaňkováDOI: 10.14736/kyb-2017-6-1131

Abstract:

%Abstract of the paper (not exceeding 250 words) summarizing the principle %technique and conclusions. Because the abstract must be able to stand independently, mathematical formulas and bibliographical references %should be kept to a minimum. 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

Classification:

46N10, 47N10, 65K10, 90C25, 90C30, 90C90

paper.pdf

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