Abstract
Using multiresolution based on Harten’s framework [J. Appl. Numer. Math., 12 (1993), pp. 153–192.] we introduce an alternative to construct a prediction operator using Learning statistical theory. This integrates two ideas: generalized wavelets and learning methods, and opens several possibilities in the compressed signal context. We obtain theoretical results which prove that this type of schemes (LMR schemes) are equal to or better than the classical schemes. Finally, we compare traditional methods with the algorithm that we present in this paper.
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Aràndiga, F., Cohen, A., Yáñez, D.F. (2012). Design of Multiresolution Operators Using Statistical Learning Tools: Application to Compression of Signals. In: Boissonnat, JD., et al. Curves and Surfaces. Curves and Surfaces 2010. Lecture Notes in Computer Science, vol 6920. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27413-8_6
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DOI: https://doi.org/10.1007/978-3-642-27413-8_6
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