Authors
Hui Zhou (University of Minnesota,USA)
Lingzhou Xue (Penn State University, USA)
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 has been proposed and studied. Sparse PCA is shown to offer a `right' solution under high-dimensions. In this article, we review methodological and theoretical developments of sparse PCA, as well as its applications in scientific studies.