Big Data Dimension Reduction Using PCA

Principal component analysis (PCA) is a powerful tool in dimensional reduction for highly correlated data. Classical PCA approaches cannot be applied to big data because ofmemory and storage barriers. To solve the problem, the article proposes a new approach. The basic idea is to derive an array of sufficient statistics by scanning data by rows. It shows that the proposed approach can provide exact solutions if the linear regression approach is used in the follow up analysis.