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We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k << N significant Fourier coefficients from a signal of bandwidth N.
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A. Gilbert, S. Guha, P. Indyk, S. Muthukrishnan, and M. Strauss, Near-optimal sparse Fourier representations via sampling , Symposium on Theory of Computing, 2002, pp. 152–161
2002
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A. Akavia, S. Goldwasser, and S. Safra, Proving hard-core predicates using list decoding , Annual Symposium on Foundations of Computer Science, vol. 44, 2003, pp. 146–159
2003
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I.E. Shparlinski and R. Steinfeld, Noisy chinese remaindering in the Lee norm , Journal of Complexity 20
2004
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M. Frigo and S. G. Johnson, The design and implementation of FFTW3 , Proceedings of the IEEE 93
2005
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2008
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A. Cohen, W. Dahmen, and R. DeVore, Compressed sensing and best k k -term approximation , J. AMS 22
2009
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D. P. Dubhashi and A. Panconesi, Concentration of measure for the analysis of randomized algorithms , Cambridge University Press, Cambridge, 2009
2009
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A. Akavia, Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions , Conference on Learning Theory (CoLT), 2010
2010
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H. Hassanieh, P. Indyk, D. Katabi, and E. Price, Nearly optimal sparse fourier transform , to appear in ACM Symposium on Theory of Computing (STOC), 2012
2012
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