TY - JOUR
T1 - A Selective Overview of Sparse Principal Component Analysis
AU - Zou, Hui
AU - Xue, Lingzhou
N1 - Funding Information:
Manuscript received January 30, 2018; revised May 28, 2018; accepted June 8, 2018. Date of current version August 2, 2018. The work of H. Zou was supported in part by the National Science Foundation (NSF) under Grant DMS-1505111. The work of L. Xue was supported by the National Science Foundation (NSF) under Grant DMS-1505256. (Corresponding author: Hui Zou.) H. Zou is with the Department of Statistics, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: zouxx019@umn.edu). L. Xue is with the Pennsylvania State University, State College, PA 16801 USA (e-mail: lzxue@psu.edu).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - Principal component analysis (PCA) is a widely used technique for dimension reduction, data processing, and feature extraction. The three tasks are particularly useful and important in high-dimensional data analysis and statistical learning. However, the regular PCA encounters great fundamental challenges under high dimensionality and may produce 'wrong' results. As a remedy, sparse PCA (SPCA) has been proposed and studied. SPCA is shown to offer a 'right' solution under high dimensions. In this paper, we review methodological and theoretical developments of SPCA, as well as its applications in scientific studies.
AB - Principal component analysis (PCA) is a widely used technique for dimension reduction, data processing, and feature extraction. The three tasks are particularly useful and important in high-dimensional data analysis and statistical learning. However, the regular PCA encounters great fundamental challenges under high dimensionality and may produce 'wrong' results. As a remedy, sparse PCA (SPCA) has been proposed and studied. SPCA is shown to offer a 'right' solution under high dimensions. In this paper, we review methodological and theoretical developments of SPCA, as well as its applications in scientific studies.
UR - http://www.scopus.com/inward/record.url?scp=85050176747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050176747&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2018.2846588
DO - 10.1109/JPROC.2018.2846588
M3 - Article
AN - SCOPUS:85050176747
SN - 0018-9219
VL - 106
SP - 1311
EP - 1320
JO - Proceedings of the Institute of Radio Engineers
JF - Proceedings of the Institute of Radio Engineers
IS - 8
M1 - 8412518
ER -