Search Results for author:
Found 12 papers, 7 papers with code
Matrix Approximation with Side Information: When Column Sampling is Enough
A theoretical spectral error bound is provided, which captures the possible inaccuracies of the side information.
Uncertainty-Based Non-Parametric Active Peak Detection
Active, non-parametric peak detection is considered.
Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise
An important setting where this is useful is for linearly data dependent noise that is sparse with support that changes enough over time.
Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data
In this work we study the problem of Subspace Tracking with missing data (ST-miss) and outliers (Robust ST-miss).
Provable Low Rank Phase Retrieval
We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix $X^*$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns.
Provable Subspace Tracking from Missing Data and Matrix Completion
In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
Static and Dynamic Robust PCA and Matrix Completion: A Review
Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers.
Nearly Optimal Robust Subspace Tracking
We prove that NORST solves both the RST and the dynamic RPCA problems under weakened standard RPCA assumptions, two simple extra assumptions (slow subspace change and most outlier magnitudes lower bounded), and a few minor assumptions.
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA).
Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise
This work obtains novel finite sample guarantees for Principal Component Analysis (PCA).
Provable Dynamic Robust PCA or Robust Subspace Tracking
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees
We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, as a result, the noise and the true data are correlated.
