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Xiaoming Huo
Researcher at Georgia Institute of Technology
Publications - 117
Citations - 5052
Xiaoming Huo is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Computer science & Computational complexity theory. The author has an hindex of 25, co-authored 98 publications receiving 4743 citations. Previous affiliations of Xiaoming Huo include Stanford University.
Papers
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Journal ArticleDOI
Uncertainty principles and ideal atomic decomposition
David L. Donoho,Xiaoming Huo +1 more
TL;DR: It is proved that if S is representable as a highly sparse superposition of atoms from this time-frequency dictionary, then there is only one such highly sparse representation of S, and it can be obtained by solving the convex optimization problem of minimizing the l/sup 1/ norm of the coefficients among all decompositions.
Journal ArticleDOI
Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
Jie Chen,Xiaoming Huo +1 more
TL;DR: Simulations show that the predictions made by the proved theorems tend to be very conservative; this is consistent with some recent advances in probabilistic analysis based on random matrix theory.
Book ChapterDOI
Beamlets and Multiscale Image Analysis
David L. Donoho,Xiaoming Huo +1 more
TL;DR: A framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis is described.
Book
Near-optimal detection of geometric objects by fast multiscale methods
TL;DR: A general approach to detectors for "geometric" objects in noisy data is described, which covers several classes of geometrically defined signals, and allows for asymptotically optimal detection thresholds and fast algorithms for near-optimal detectors.
Journal ArticleDOI
Fast Computing for Distance Covariance
Xiaoming Huo,Gábor J. Székely +1 more
TL;DR: In this article, the authors showed that the computation of distance covariance and distance correlation of real-valued random variables can be done in O(n log n) time using a U-statistic.