TY  - JOUR
ID  - SisLab4561
UR  - https://rev-jec.org/index.php/rev-jec/article/view/270
IS  - 1-2
A1  - Le, Trung Thanh
A1  - Nguyen, Viet Dung
A1  - Nguyen, Linh Trung
A1  - Abed-Meraim, Karim
Y1  - 2021/06//
N2  - Principal component analysis (PCA) and subspace estimation (SE) are popular data analysis tools and used in a wide range of applications. The main interest in PCA/SE is for dimensionality reduction and low-rank approximation purposes. The emergence of big data streams have led to several essential issues for performing PCA/SE. Among them are (i) the size of such data streams increases over time, (ii) the underlying models may be time-dependent, and iii) problem of dealing with the uncertainty and incompleteness in data. A robust variant of PCA/SE for such data streams, namely robust online PCA or robust subspace tracking (RST), has been introduced as a good alternative. The main goal of this paper is to provide a brief survey on recent RST algorithms in signal processing. Particularly, we begin this survey by introducing the basic ideas of the RST problem. Then, different aspects of RST are reviewed with respect to different kinds of non-Gaussian noises and sparse constraints. Our own contributions on this topic are also highlighted.
PB  - REV
JF  - REV Journal on Electronics and Communications
VL  - 11
SN  - 1859-387X
TI  - Robust Subspace Tracking Algorithms in Signal Processing: A Brief Survey
SP  - 16
AV  - none
EP  - 25
ER  -