Principal component analysis for big data

Understand

Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences.

• The complexity of big data often makes dimension reduction techniques necessary before conducting statistical inference.
• Principal component analysis, commonly referred to as PCA, has become an essential tool for multivariate data analysis and unsupervised dimension reduction, the goal of which is to find a lower dimensional subspace that captures most of the variation in the dataset.
• This article provides an overview of methodological and theoretical developments of PCA over the last decade, with focus on its applications to big data analytics.