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math.ST2022★ 1 cited
Sketching low-rank matrices with a shared column space by convex programming
Rakshith S Srinivasa, Seonho Kim, Kiryung Lee
In many practical applications including remote sensing, multi-task learning, and multi-spectrum imaging, data are described as a set of matrices sharing a common column space. We…
math.ST2021★ 1 cited
Approximately low-rank recovery from noisy and local measurements by convex program
Kiryung Lee, Rakshith Sharma Srinivasa, Marius Junge +1
Low-rank matrix models have been universally useful for numerous applications, from classical system identification to more modern matrix completion in signal processing and statis…
math.ST2020
Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimization
Sohail Bahmani, Kiryung Lee
We study an estimator with a convex formulation for recovery of low-rank matrices from rank-one projections. Using initial estimates of the factors of the target ma…