356 citations · 528 across the 12 of their papers we have counts for
12 papers
Matrix Approximation with Side Information: When Column Sampling is Enough
Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac +2
A novel matrix approximation problem is considered herein: observations based on a few fully sampled columns and quasi-polynomial structural side information are exploited. The fra…
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…
Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise
Praneeth Narayanamurthy, Namrata Vaswani
This work studies the robust subspace tracking (ST) problem. Robust ST can be simply understood as a (slow) time-varying subspace extension of robust PCA. It assumes that the true…
Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data
Praneeth Narayanamurthy, Namrata Vaswani, Aditya Ramamoorthy
In this work we study the problem of Subspace Tracking with missing data (ST-miss) and outliers (Robust ST-miss). We propose a novel algorithm, and provide a guarantee for both the…
Provable Low Rank Phase Retrieval
Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an matrix of rank from a different and independent set of phaseless (mag…
Provable Subspace Tracking from Missing Data and Matrix Completion
Praneeth Narayanamurthy, Vahid Daneshpajooh, Namrata Vaswani
We study the problem of subspace tracking in the presence of missing data (ST-miss). In recent work, we studied a related problem called robust ST. In this work, we show that a sim…
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…
Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise
Namrata Vaswani, Praneeth Narayanamurthy
This work obtains novel finite sample guarantees for Principal Component Analysis (PCA). These hold even when the corrupting noise is non-isotropic, and a part (or all of it) is da…
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…