Optimal sampling and curve interpolation via wavelets

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We propose a wavelet-based method for determining optimal sampling positions and inferring underlying functions based on the samples when it is known that the underlying function is Lipschitz. We first propose a Lipschitz regularity-based statistical model for data which are sampled from a Lipschitz curve. And then we propose a wavelet-based interpolation method for generating a Lipschitz curve given a set of points, and derive the optimal sampling positions. (C) 2013 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2013-07
Language
English
Article Type
Article
Citation

APPLIED MATHEMATICS LETTERS, v.26, no.7, pp.774 - 779

ISSN
0893-9659
DOI
10.1016/j.aml.2013.03.002
URI
http://hdl.handle.net/10203/254764
Appears in Collection
IE-Journal Papers(저널논문)
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