E
Emmanuel J. Candès
Researcher at Stanford University
Publications - 280
Citations - 148481
Emmanuel J. Candès is an academic researcher from Stanford University. The author has contributed to research in topics: Convex optimization & Compressed sensing. The author has an hindex of 102, co-authored 262 publications receiving 135077 citations. Previous affiliations of Emmanuel J. Candès include Samsung & École Normale Supérieure.
Papers
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Exact matrix completion via convex optimization
TL;DR: In this paper, a convex programming problem is used to find the matrix with the minimum nuclear norm that is consistent with the observed entries in a low-rank matrix, which is then used to recover all the missing entries from most sufficiently large subsets.
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The curvelet transform for image denoising
TL;DR: In this paper, the authors describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform, which offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity.
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The Power of Convex Relaxation: Near-Optimal Matrix Completion
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: This paper shows that, under certain incoherence assumptions on the singular vectors of the matrix, recovery is possible by solving a convenient convex program as soon as the number of entries is on the order of the information theoretic limit (up to logarithmic factors).
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Sparsity and incoherence in compressive sampling
TL;DR: It is shown that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the numberof nonzero components in x 0, and µ is the largest entry in U properly normalized: µ(U) = p n · maxk,j |Uk,j|.
Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges
TL;DR: The basic issues of efficient m-term approximation, the construction of efficient adaptive representation, theConstruction of the curvelet frame, and a crude analysis of the performance of curvelet schemes are explained.