Large-Scale Paralleled Sparse Principal Component Analysis

TL;DR

This paper introduces a GPU-accelerated parallel method for Sparse PCA, significantly improving computational efficiency while maintaining the interpretability of principal components in large-scale data analysis.

Contribution

The paper presents the first parallel GPU implementation of the generalized power method for Sparse PCA, achieving up to eleven times faster processing than CPU versions.

Findings

GPU implementation is up to 11 times faster than CPU

GPU version is up to 107 times faster than MATLAB implementation

Sparse PCA provides practical advantages in real-world datasets

Abstract

Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretability by approximating sparse PCs whose projections capture the maximal variance of original data. In this paper we present an efficient and paralleled method of SPCA using graphics processing units (GPUs), which can process large blocks of data in parallel. Specifically, we construct parallel implementations of the four optimization formulations of the generalized power method of SPCA (GP-SPCA), one of the most efficient and effective SPCA approaches, on a GPU. The parallel GPU implementation of GP-SPCA (using CUBLAS) is up to eleven times faster than the corresponding CPU implementation (using CBLAS), and up to 107 times faster than a MatLab implementation. Extensive comparative experiments in several real-world datasets confirm that SPCA offers a practical advantage.