Piecewise-polynomial signal segmentation using proximal splitting convex optimization methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU119954" target="_blank" >RIV/00216305:26220/16:PU119954 - isvavai.cz</a>
Result on the web
<a href="http://www.utko.feec.vutbr.cz/~rajmic/talks/APMOD_2016-Novosadova.pdf" target="_blank" >http://www.utko.feec.vutbr.cz/~rajmic/talks/APMOD_2016-Novosadova.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Piecewise-polynomial signal segmentation using proximal splitting convex optimization methods
Original language description
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.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů