Sparse Fourier Transform in Any Constant Dimension with Nearly-Optimal Sample Complexity in Sublinear Time

Abstract

We consider the problem of computing a <i>k</i>-sparse approximation to the Fourier transform of a length <i>N</i> signal. Our main result is a randomized algorithm for computing such an approximation (i.e. achieving &#x2113;<sub>2</sub>/&#x2113;<sub>2</sub> sparse recovery guarantees using Fourier measurements) using <i>O</i><sub><i>d</i></sub>(<i>k</i>log… (More)
DOI: 10.1145/2897518.2897650

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Cite this paper

@inproceedings{Kapralov2016SparseFT, title={Sparse Fourier Transform in Any Constant Dimension with Nearly-Optimal Sample Complexity in Sublinear Time}, author={Mikhail Kapralov}, booktitle={STOC}, year={2016} }