356 citations · 528 across the 12 of their papers we have counts for
6 papers · 1 filter
Uncertainty-Based Non-Parametric Active Peak Detection
Praneeth Narayanamurthy, Urbashi Mitra
Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localiz…
Static and Dynamic Robust PCA and Matrix Completion: A Review
Namrata Vaswani, Praneeth Narayanamurthy
Principal Components Analysis (PCA) is one of the most widely used dimension reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by…
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy, Namrata Vaswani
In this work, we study the robust subspace tracking (RST) problem and obtain one of the first two provable guarantees for it. The goal of RST is to track sequentially arriving data…
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
Namrata Vaswani, Thierry Bouwmans, Sajid Javed +1
PCA is one of the most widely used dimension reduction techniques. A related easier problem is "subspace learning" or "subspace estimation". Given relatively clean data, both are e…
Provable Dynamic Robust PCA or Robust Subspace Tracking
Praneeth Narayanamurthy, Namrata Vaswani
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lies in a low-dimensional subspace that can chan…
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees
Namrata Vaswani, Praneeth Narayanamurthy
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. Under…