Sparse Principal Component Analysis

Title: Sparse Principal Component Analysis
Creator(s): Zou, Hui; Hastie, Trevor; Tibshirani, Robert
Date: 2006
Is Part Of: Journal of Computational and Graphical Statistics
Volume: 15
Issue: 2
Pages: 265-286
Language: eng
ISSN: 1061-8600
URI: https://www.jstor.org/stable/27594179
Zotero BiDiAr: https://www.zotero.org/groups/5293298/bidiar/items/YK97YUJJitem-list
Abstract: Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We first show that PCA can be formulated as a regression-type optimization problem; sparse loadings are then obtained by imposing the lasso (elastic net) constraint on the regression coefficients. Efficient algorithms are proposed to fit our SPCA models for both regular multivariate data and gene expression arrays. We also give a new formula to compute the total variance of modified principal components. As illustrations, SPCA is applied to real and simulated data with encouraging results.

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