187 citations · 346 across the 26 of their papers we have counts for
17 papers · 1 filter
Phase Retrieval of Low-Rank Matrices by Anchored Regression
Kiryung Lee, Sohail Bahmani, Yonina Eldar +1
We study the low-rank phase retrieval problem, where we try to recover a low-rank matrix from a series of phaseless linear measurements. This is a fourth-order inve…
Optimal Sample Complexity for Stable Matrix Recovery
Yanjun Li, Kiryung Lee, Yoram Bresler
Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper fills a theoretical gap in matrix rec…
Blind Gain and Phase Calibration via Sparse Spectral Methods
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly…
Spectral Methods for Passive Imaging: Non-asymptotic Performance and Robustness
Kiryung Lee, Felix Krahmer, Justin Romberg
We study the problem of passive imaging through convolutive channels. A scene is illuminated with an unknown, unstructured source, and the measured response is the convolution of t…
Unified Theory for Recovery of Sparse Signals in a General Transform Domain
Kiryung Lee, Yanjun Li, Kyong Hwan Jin +1
Compressed sensing provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that practical algorithms provide robust recovery from noisy linear measur…
Optimal Sample Complexity for Stable Matrix Recovery
Yanjun Li, Kiryung Lee, Yoram Bresler
Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper fills a theoretical gap in matrix rec…
Optimal Sample Complexity for Blind Gain and Phase Calibration
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo…
Blind Recovery of Sparse Signals from Subsampled Convolution
Kiryung Lee, Yanjun Li, Marius Junge +1
Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors ta…
RIP-like Properties in Subsampled Blind Deconvolution
Kiryung Lee, Marius Junge
We derive near optimal performance guarantees for subsampled blind deconvolution. Blind deconvolution is an ill-posed bilinear inverse problem and additional subsampling makes the…
Compressive Sampling using Annihilating Filter-based Low-Rank Interpolation
Jong Chul Ye, Jong Min Kim, Kyong Hwan Jin +1
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes a form of invers…
Identifiability and Stability in Blind Deconvolution under Minimal Assumptions
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind deconvolution (BD) arises in many applications. Without assumptions on the signal and the filter, BD does not admit a unique solution. In practice, subspace or sparsity assum…
Identifiability in Blind Deconvolution with Subspace or Sparsity Constraints
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises in many applications. Without further constraints, BD is ill-posed. In practice, s…
A Unified Framework for Identifiability Analysis in Bilinear Inverse Problems with Applications to Subspace and Sparsity Models
Yanjun Li, Kiryung Lee, Yoram Bresler
Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usuall…
Near Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices via Sparse Power Factorization
Kiryung Lee, Yihong Wu, Yoram Bresler
Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question i…
Oblique Pursuits for Compressed Sensing
Kiryung Lee, Yoram Bresler, Marius Junge
Compressed sensing is a new data acquisition paradigm enabling universal, simple, and reduced-cost acquisition, by exploiting a sparse signal model. Most notably, recovery of the s…
Subspace Methods for Joint Sparse Recovery
Kiryung Lee, Yoram Bresler, Marius Junge
We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from the…
Guaranteed Minimum Rank Approximation from Linear Observations by Nuclear Norm Minimization with an Ellipsoidal Constraint
Kiryung Lee, Yoram Bresler
The rank minimization problem is to find the lowest-rank matrix in a given set. Nuclear norm minimization has been proposed as an convex relaxation of rank minimization. Recht, Faz…