
Fast and SampleEfficient Federated Low Rank Matrix Recovery from Columnwise Linear and Quadratic Projections
This work studies the following problem and its magnitudeonly extension...
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NonConvex Structured Phase Retrieval
Phase retrieval (PR), also sometimes referred to as quadratic sensing, i...
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Fast Robust Subspace Tracking via PCA in Sparse DataDependent Noise
This work studies the robust subspace tracking (ST) problem. Robust ST c...
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SampleEfficient Low Rank Phase Retrieval
In this paper we obtain an improved guarantee for the Low Rank Phase Ret...
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Federated OvertheAir Subspace Learning from Incomplete Data
Federated learning refers to a distributed learning scenario in which us...
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Random Convolutional Coding for Robust and Straggler Resilient Distributed Matrix Computation
Distributed matrix computations (matrixvector and matrixmatrix multipl...
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Phaseless Low Rank Matrix Recovery and Subspace Tracking
This work introduces the first simple and provably correct solution for ...
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Subspace Tracking from Missing and Outlier Corrupted Data
We study the related problems of subspace tracking in the presence of mi...
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Phaseless Subspace Tracking
This work takes the first steps towards solving the "phaseless subspace ...
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Static and Dynamic Robust PCA via LowRank + Sparse Matrix Decomposition: A Review
Principal Components Analysis (PCA) is one of the most widely used dimen...
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MEDRoP: MemoryEfficient Dynamic Robust PCA
Robust PCA (RPCA) is the problem of separating a given data matrix into ...
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Robust PCA and Robust Subspace Tracking
Principal Components Analysis (PCA) is one of the most widely used dimen...
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Video Denoising and Enhancement via Dynamic Video Layering
Video denoising refers to the problem of removing "noise" from a video s...
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Finite Sample Guarantees for PCA in NonIsotropic and DataDependent Noise
This work obtains novel finite sample guarantees for Principal Component...
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Provable Dynamic Robust PCA or Robust Subspace Tracking
Dynamic robust PCA refers to the dynamic (timevarying) extension of the...
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PCA in DataDependent Noise (CorrelatedPCA): Nearly Optimal Finite Sample Guarantees
We study Principal Component Analysis (PCA) in a setting where a part of...
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CorrelatedPCA: Principal Components' Analysis when Data and Noise are Correlated
Given a matrix of observed data, Principal Components Analysis (PCA) com...
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Online Matrix Completion and Online Robust PCA
This work studies two interrelated problems  online robust PCA (RPCA) a...
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Exact Reconstruction Conditions for Regularized Modified Basis Pursuit
In this correspondence, we obtain exact recovery conditions for regulari...
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Realtime Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank m...
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