7 accepted papers
In prior work, it was shown that high performance matrix approximation/completion was possible when only a few fully sampled columns were available of a ground truth matrix if there was appropriate side information on the rowspace of the matrix. Several applications from quantum chemistry, magnetic…
A novel matrix completion problem is considered herein: observations based on fully sampled columns and quasi-polynomial side information is exploited. The framework is motivated by quantum chemistry problems wherein full matrix computation is expensive, but partial computations only lead to column…
Robust Subspace Tracking with missing data (RST-miss) has been extensively studied in the past decade. In this work we study RST-miss to the setting where the data is federated and when the over-air data communication modality is used for information exchange between the K peer nodes and the central…
This work proposes the first set of simple, practically useful, and provable algorithms for two inter-related problems. (i) The first is low-rank matrix recovery from magnitude-only (phaseless) linear projections of each of its columns. This finds important applications in phaseless dynamic imaging,…
Robust Matrix Completion (RMC) is the problem of estimating a low-rank matrix in the presence of missing entries and element-wise (sparse) outliers. In this work, we study the RMC problem with the extra assumption that the clean data is generated from either a fixed or a slowly-changing low-dimensio…
Robust PCA (RPCA) is the problem of separating a given data matrix into the sum of a sparse matrix and a low-rank matrix. Static RPCA is the RPCA problem in which the subspace from which the true data is generated remains fixed over time. Dynamic RPCA instead assumes that the subspace can change wit…
Robust subspace tracking (RST) can be simply understood as a dynamic (time-varying) extension of robust PCA. More precisely, it is the problem of tracking data lying in a fixed or slowly-changing low-dimensional subspace while being robust to sparse outliers. This work develops a recursive projected…