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FOCM

2010

2010

We study the problem of estimating the best k term Fourier representation for a given frequency-sparse signal (i.e., vector) A of length N k. More explicitly, we investigate how to deterministically identify k of the largest magnitude frequencies of ˆA, and estimate their coefﬁcients, in polynomial(k, log N) time. Randomized sublinear time algorithms which have a small (controllable) probability of failure for each processed signal exist for solving this problem [24, 25]. In this paper we develop the ﬁrst known deterministic sublinear time sparse Fourier Transform algorithm which is guaranteed to produce accurate results. As an added bonus, a simple relaxation of our deterministic Fourier result leads to a new Monte Carlo Fourier algorithm with similar runtime/sampling bounds to the current best randomized Fourier method [25]. Finally, the Fourier algorithm we develop here implies a simpler optimized version of the deterministic compressed sensing method previously developed in [...

Related Content

Added |
25 Jan 2011 |

Updated |
25 Jan 2011 |

Type |
Journal |

Year |
2010 |

Where |
FOCM |

Authors |
Mark A. Iwen |

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