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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Journal ArticleDOI
Solving Quadratic Equations via PhaseLift When There Are About as Many Equations as Unknowns
Emmanuel J. Candès,Xiaodong Li +1 more
TL;DR: It is shown that any complex vector can be recovered exactly from on the order of n quadratic equations of the form |〈ai,x0〉|2=bi, i=1,…,m, by using a semidefinite program known as PhaseLift, improving upon earlier bounds.
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Compressive fluorescence microscopy for biological and hyperspectral imaging
Vincent Studer,Vincent Studer,Jérôme Bobin,Makhlad Chahid,Makhlad Chahid,Hamed Shams Mousavi,Hamed Shams Mousavi,Emmanuel J. Candès,Maxime Dahan +8 more
TL;DR: An implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging is presented and the potential benefits of CS acquisition for higher-dimensional signals are illustrated, which typically exhibits extreme redundancy.
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A Geometric Analysis of Subspace Clustering with Outliers
TL;DR: In this paper, a geometric analysis of sparse subspace clustering (SSC) is presented, showing that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension.
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A geometric analysis of subspace clustering with outliers
TL;DR: A novel geometric analysis of an algorithm named sparse subspace clustering (SSC) is developed, which signicantly broadens the range of problems where it is provably eective and shows that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension.
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Recovering edges in ill-posed inverse problems: optimality of curvelet frames
TL;DR: It is proved that the curvelet shrinkage can be tuned so that the estimator will attain, within logarithmic factors, the MSE $O(\varepsilon^{4/5})$ as noise level $\varePSilon\to 0$.