A New Method for Principal Component Analysis of High-Dimensional Data Using Compressive Sensing

Principal Component Analysis of high dimensional data often runs into time and memory limitations. This is especially the case if the dimension and the number of data set elements are of about the same size. The authors propose a new method to calculate Principal Components based on Compressive Sensing. Compressive Sensing can be interpreted as a new method for data compression with a number of positive features for application in Statistics. They will demonstrate its usability for Principal Component Analysis by mentioning relevant results from literature and show their results for real world functional data.