T
Terence Tao
Researcher at University of California, Los Angeles
Publications - 625
Citations - 101657
Terence Tao is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Conjecture & Bounded function. The author has an hindex of 111, co-authored 606 publications receiving 94316 citations. Previous affiliations of Terence Tao include Hokkaido University & Princeton University.
Papers
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
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Decoding by linear programming
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: F can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program) and numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant fraction of the output is corrupted.
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Stable signal recovery from incomplete and inaccurate measurements
TL;DR: In this paper, the authors considered the problem of recovering a vector x ∈ R^m from incomplete and contaminated observations y = Ax ∈ e + e, where e is an error term.
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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.
Posted Content
Stable Signal Recovery from Incomplete and Inaccurate Measurements
TL;DR: It is shown that it is possible to recover x0 accurately based on the data y from incomplete and contaminated observations.