TY - JOUR
T1 - Robust subspace learning
T2 - Robust PCA, robust subspace tracking, and robust subspace recovery
AU - Vaswani, Namrata
AU - Bouwmans, Thierry
AU - Javed, Sajid
AU - Narayanamurthy, Praneeth
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA). For long data sequences, if one tries to use a single lower-dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of RSL and tracking.
AB - Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA). For long data sequences, if one tries to use a single lower-dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of RSL and tracking.
UR - http://www.scopus.com/inward/record.url?scp=85049364694&partnerID=8YFLogxK
U2 - 10.1109/MSP.2018.2826566
DO - 10.1109/MSP.2018.2826566
M3 - Article
AN - SCOPUS:85049364694
SN - 1053-5888
VL - 35
SP - 32
EP - 55
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 4
ER -