Boaz Nadler, Finite Sample Approximation Results for Principal Component Analysis: A Matrix Perturbation Approach, The Annals of Statistics, Vol. 36, No. 6, High Dimensional Inference and Random ...

We extend the principal component analysis (PCA) to second-order stationary vector time series in the sense that we seek for a contemporaneous linear transformation for a p-variate time series such ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as ...