J
Joel A. Tropp
Researcher at California Institute of Technology
Publications - 182
Citations - 53704
Joel A. Tropp is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Matrix (mathematics) & Convex optimization. The author has an hindex of 67, co-authored 173 publications receiving 49525 citations. Previous affiliations of Joel A. Tropp include Rice University & University of Michigan.
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An Introduction to Matrix Concentration Inequalities
TL;DR: The aim of this monograph is to describe the most successful methods from this area along with some interesting examples that these techniques can illuminate.
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Freedman's inequality for matrix martingales
TL;DR: Oliveira et al. as mentioned in this paper showed that the large deviation behavior of a martingale is controlled by the predictable quadratic variation and a uniform upper bound for the Martingale difference sequence.
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Constructing packings in grassmannian manifolds via alternating projection
TL;DR: The alternating projection method can be used to produce packings of subspaces in real and complex Grassmannian spaces equipped with the Fubini–Study distance and can prove that some of the novel configurations constructed by the algorithm have packing diameters that are nearly optimal.
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Two Proposals for Robust PCA using Semidefinite Programming
Michael B. McCoy,Joel A. Tropp +1 more
TL;DR: Two novel approaches for robust principal component analysis based on semidefinite programming are proposed, the first of which seeks directions of large spread in the data while damping the effect of outliers, and the second produces a low-leverage decomposition of the data that attempts to form a high-rank model for the data by separating out corrupted observations.