A Selective Overview of Sparse Principal Component Analysis

Hui Zou, Lingzhou Xue

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number8412518
Pages (from-to)1311-1320
Number of pages10
JournalProceedings of the IEEE
Volume106
Issue number8
DOIs
StatePublished - Aug 2018

Fingerprint

Principal component analysis
Feature extraction

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{d38e6e76ffd74ae1b9a2cabd88b3a695,
title = "A Selective Overview of Sparse Principal Component Analysis",
abstract = "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.",
author = "Hui Zou and Lingzhou Xue",
year = "2018",
month = "8",
doi = "10.1109/JPROC.2018.2846588",
language = "English (US)",
volume = "106",
pages = "1311--1320",
journal = "Proceedings of the IEEE",
issn = "0018-9219",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

A Selective Overview of Sparse Principal Component Analysis. / Zou, Hui; Xue, Lingzhou.

In: Proceedings of the IEEE, Vol. 106, No. 8, 8412518, 08.2018, p. 1311-1320.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Selective Overview of Sparse Principal Component Analysis

AU - Zou, Hui

AU - Xue, Lingzhou

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

VL - 106

SP - 1311

EP - 1320

JO - Proceedings of the IEEE

JF - Proceedings of the IEEE

SN - 0018-9219

IS - 8

M1 - 8412518

ER -