Joint talk with Lieven De Lathauwer (Katholieke Universiteit Leuven): Spline-based separable expansions for approximation, regression and classification

Nithin Govindarajan
KU Leuven

We introduce a framework for the modelling of multivariate functions that are well approximated by a sum of (few) separable terms. The framework proposes to approximate each component function by a B-spline, resulting in an approximant where the underlying coefficient tensor of the tensor product expansion has a low rank polyadic decomposition parametrization. By exploiting the multilinear structure, as well as the sparsity pattern of the compactly supported B-spline basis terms, we demonstrate how such an approximant is well-suited for approximation, regression and classification tasks by using the Gauss–Newton algorithm as a workhorse to train the parameters. Numerical examples will illustrate the effectiveness of the approach.

This is joint work with Nithin Govindarajan (KU Leuven) and Nico Vervliet (KU Leuven).

Presentation (PDF File)

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