26 October 1999 Wavelet interpolation networks for hierarchical approximation
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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.
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Christophe P. Bernard, Stephane G. Mallat, Jean-Jeacques E. Slotine, "Wavelet interpolation networks for hierarchical approximation", Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); doi: 10.1117/12.366782; https://doi.org/10.1117/12.366782
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