About sparsity in functional relevance learning in generalized learning vector quantization

We propose a functional approach to relevance learning and matrix adaptation for learning vector quantization of high-dimensional functional data. We show how parametrization of the functional relevance profile or functional matrix learning can be established for a reasonable number of adaptive parameters. In particular we empha-size model sparsity in terms of structural sparsity and feature selection