A Selective Overview of Sparse Principal Component Analysis

Hui Zou, Lingzhou Xue

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A Selective Overview of Sparse Principal Component Analysis'. Together they form a unique fingerprint.

Cite this