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Low rank matrix recovery is about reconstructing a low rank matrix from incomplete measurements. It arises frequently in many research areas of science and engineering, for example, machine learning, ...
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix from just a few measurements consisting of linear combinations of the matrix entries. We show that pro...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximate QR and SVD factorizations, has recently become an intense area of research. This paper studies one...
Here we show that the problem of realizing a polytope with specified combinatorics is equivalent to a low rank matrix completion problem.This is comparable to known results reducing realizability to s...
Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data anal- ysis, has been successfully used in several applications, such as in collaborative filtering to design reco...
A problem of estimation of a Hermitian nonnegatively definite matrix ρ of unit trace (for instance, a density matrix of a quantum system) based on n independent measurements Yj = tr(ρXj) + ξj , j = 1...

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