Correlated-PCA: Principal Components' Analysis when Data and Noise are
Correlated

v1v2 (latest)

Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

Neural Information Processing Systems (NeurIPS), 2016

15 August 2016

Namrata Vaswani

ArXiv (abs)PDF HTML

Papers citing "Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated"

9 / 9 papers shown

Title
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise ModelingPattern Recognition (Pattern Recognit.), 2019 Chihao Zhang Kuo Gai Shihua Zhang 197 16 0 25 Nov 2019
Heteroskedastic PCA: Algorithm, Optimality, and Applications Anru R. Zhang T. Tony Cai Yihong Wu 412 79 0 19 Oct 2018
Static and Dynamic Robust PCA and Matrix Completion: A ReviewProceedings of the IEEE (Proc. IEEE), 2018 Namrata Vaswani Praneeth Narayanamurthy 222 78 0 01 Mar 2018
Nearly Optimal Robust Subspace Tracking Praneeth Narayanamurthy Namrata Vaswani 257 9 0 17 Dec 2017
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery Namrata Vaswani T. Bouwmans S. Javed Praneeth Narayanamurthy OOD 365 295 0 26 Nov 2017
Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise Namrata Vaswani Praneeth Narayanamurthy 146 21 0 19 Sep 2017
Provable Dynamic Robust PCA or Robust Subspace Tracking Praneeth Narayanamurthy Namrata Vaswani 242 57 0 24 May 2017
Asymptotic performance of PCA for high-dimensional heteroscedastic data David Hong Laura Balzano Jeffrey A. Fessler 312 62 0 20 Mar 2017
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees Namrata Vaswani Praneeth Narayanamurthy 211 2 0 10 Feb 2017