A New Method for Principal Component Analysis of High-Dimensional Data Using Compressive Sensing
نویسندگان
چکیده
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 is of about the same size. We 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. We will demonstrate its usability for Principal Component Analysis by mentioning relevant results from literature and show our results for real world functional data.
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