2022
DOI: 10.48550/arxiv.2202.02360
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Towards optimal sampling for learning sparse approximation in high dimensions

Abstract: In this chapter, we discuss recent work on learning sparse approximations to high-dimensional functions on data, where the target functions may be scalar-, vector-or even Hilbert space-valued. Our main objective is to study how the sampling strategy affects the sample complexity -that is, the number of samples that suffice for accurate and stable recovery -and to use this insight to obtain optimal or near-optimal sampling procedures. We consider two settings. First, when a target sparse representation is known… Show more

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