A Selective Overview of Sparse Principal Component Analysis

Volume: 106, Issue: 8, Pages: 1311 - 1320
Published: Jul 18, 2018
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...
Paper Details
Title
A Selective Overview of Sparse Principal Component Analysis
Published Date
Jul 18, 2018
Volume
106
Issue
8
Pages
1311 - 1320
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