Statistics and Its Interface

Volume 9 (2016)

Number 4

Special Issue on Statistical and Computational Theory and Methodology for Big Data

Guest Editors: Ming-Hui Chen (University of Connecticut); Radu V. Craiu (University of Toronto); Faming Liang (University of Florida); and Chuanhai Liu (Purdue University)

Sparse generalized principal component analysis for large-scale applications beyond Gaussianity

Pages: 521 – 533

DOI: http://dx.doi.org/10.4310/SII.2016.v9.n4.a11

Authors

Qiaoya Zhang (Department of Statistics, Florida State University, Tallahassee, Fl., U.S.A.)

Yiyuan She (Department of Statistics, Florida State University, Tallahassee, Fl., U.S.A.)

Abstract

Principal Component Analysis (PCA) is a dimension reduction technique. It produces inconsistent estimators when the dimensionality is moderate to high, which is often the problem in modern large-scale applications where algorithm scalability and model interpretability are difficult to achieve, not to mention the prevalence of missing values. While existing sparse PCA methods alleviate inconsistency, they are constrained to the Gaussian assumption of classical PCA and fail to address algorithm scalability issues. We generalize sparse PCA to the broad exponential family distributions under high-dimensional setup, with built-in treatment for missing values. Meanwhile, we propose a family of iterative sparse generalized PCA (SG-PCA) algorithms such that despite the non-convexity and non-smoothness of the optimization task, the loss function decreases in every iteration. In terms of ease and intuitive parameter tuning, our sparsity-inducing regularization is far superior to the popular Lasso. Furthermore, to promote overall scalability, accelerated gradient is integrated for fast convergence, while a progressive screening technique gradually squeezes out nuisance dimensions of a large-scale problem for feasible optimization. High-dimensional simulation and real data experiments demonstrate the efficiency and efficacy of SG-PCA.

Keywords

sparsity, low rank estimation, principal component analysis, generalized linear models, non-convex optimization, missing values, variable screening

2010 Mathematics Subject Classification

Primary 62H25, 62J12. Secondary 62H12.

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