no code implementations • 11 Dec 2022 • Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac, Shaama Mallikarjun Sharada, Urbashi Mitra
A theoretical spectral error bound is provided, which captures the possible inaccuracies of the side information.
no code implementations • 5 May 2022 • Praneeth Narayanamurthy, Urbashi Mitra
Active, non-parametric peak detection is considered.
1 code implementation • 14 Jun 2020 • Praneeth Narayanamurthy, Namrata Vaswani
An important setting where this is useful is for linearly data dependent noise that is sparse with support that changes enough over time.
1 code implementation • 28 Feb 2020 • 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).
1 code implementation • 13 Feb 2019 • Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
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.
1 code implementation • 6 Oct 2018 • Praneeth Narayanamurthy, Vahid Daneshpajooh, Namrata Vaswani
In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
no code implementations • 1 Mar 2018 • Namrata Vaswani, Praneeth Narayanamurthy
Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers.
1 code implementation • ICML 2018 • Praneeth Narayanamurthy, Namrata Vaswani
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.
no code implementations • 26 Nov 2017 • Namrata Vaswani, Thierry Bouwmans, Sajid Javed, Praneeth Narayanamurthy
The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA).
1 code implementation • 19 Sep 2017 • Namrata Vaswani, Praneeth Narayanamurthy
This work obtains novel finite sample guarantees for Principal Component Analysis (PCA).
2 code implementations • 24 May 2017 • Praneeth Narayanamurthy, Namrata Vaswani
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).
no code implementations • 10 Feb 2017 • 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.