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Proceedings Paper

Wavelet interpolation networks for hierarchical approximation
Author(s): Christophe P. Bernard; Stephane G. Mallat; Jean-Jeacques E. Slotine
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Paper Abstract

In this paper, we motivate and describe a scattered data interpolation scheme based on a hierarchical wavelet subfamily selection process named allocation. This interpolation method applies in any dimension, where it compares well to regularization techniques, especially in terms of stability, of adaptivity and of sparsity of the learned function representation. Adaptive convergence theorems are stated, and their proofs are outlined. We also describe a variant of this approach that can be incremental, and thus works as an online learning process.

Paper Details

Date Published: 26 October 1999
PDF: 14 pages
Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); doi: 10.1117/12.366782
Show Author Affiliations
Christophe P. Bernard, Ecole Polytechnique (France)
Stephane G. Mallat, Ecole Polytechnique (France)
Jean-Jeacques E. Slotine, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3813:
Wavelet Applications in Signal and Image Processing VII
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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