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dc.contributor.authorIwen, Mark
dc.contributor.authorGilbert, Anna Rebecca
dc.contributor.authorIndyk, Piotr
dc.contributor.authorSchmidt, Ludwig
dc.date.accessioned2018-02-20T15:23:05Z
dc.date.available2018-02-20T15:23:05Z
dc.date.issued2014-08
dc.identifier.issn1053-5888
dc.identifier.urihttp://hdl.handle.net/1721.1/113828
dc.description.abstractThe discrete Fourier transform (DFT) is a fundamental component of numerous computational techniques in signal processing and scientific computing. The most popular means of computing the DFT is the fast Fourier transform (FFT). However, with the emergence of big data problems, in which the size of the processed data sets can easily exceed terabytes, the "fast" in FFT is often no longer fast enough. In addition, in many big data applications it is hard to acquire a sufficient amount of data to compute the desired Fourier transform in the first place. The sparse Fourier transform (SFT) addresses the big data setting by computing a compressed Fourier transform using only a subset of the input data, in time smaller than the data set size. The goal of this article is to survey these recent developments, explain the basic techniques with examples and applications in big data, demonstrate tradeoffs in empirical performance of the algorithms, and discuss the connection between the SFT and other techniques for massive data analysis such as streaming algorithms and compressive sensing.en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/MSP.2014.2329131en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleRecent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big dataen_US
dc.typeArticleen_US
dc.identifier.citationGilbert, Anna C., et al. “Recent Developments in the Sparse Fourier Transform: A Compressed Fourier Transform for Big Data.” IEEE Signal Processing Magazine, vol. 31, no. 5, Sept. 2014, pp. 91–100.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorGilbert, Anna Rebecca
dc.contributor.mitauthorIndyk, Piotr
dc.contributor.mitauthorSchmidt, Ludwig
dc.relation.journalIEEE Signal Processing Magazineen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGilbert, Anna C.; Indyk, Piotr; Iwen, Mark; Schmidt, Ludwigen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7983-9524
dc.identifier.orcidhttps://orcid.org/0000-0002-9603-7056
mit.licenseOPEN_ACCESS_POLICYen_US


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