Matthias Jansen

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We treat bivariate nonparametric regression, where the design of experiment can be arbitrarily irregular. Our method uses second-generation wavelets built with the lifting scheme: Starting from a simple initial transform, we propose to use some predictor operators based on a generalization in two dimensions of the Lagrange interpolating polynomial. These(More)
Recently the performance of nonlinear transforms have been given a lot of attention to overcome the suboptimal n- terms approximation power of tensor product wavelet methods on higher dimensions. The suboptimal performance prevails when those transforms are used for a sparse representation of functions consisting of smoothly varying areas separated by(More)
This paper applies the idea of normal mesh techniques, utilized in CG rendering applications of smooth manifolds in 3d space, to piecewise smooth functions defined on the plane. The nonsmoothness of these functions is located along a smoothly varying curve in the domain. The nonlin-ear nature of the proposed method allows to deal with the 'regularity' of(More)
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