Principal Component Analysis (PCA) is the general name for a technique which uses sophisticated underlying mathematical principles to transforms a number of possibly correlated variables into a smaller number of variables called principal components. The origins of PCA lie in multivariate data analysis, however, it has a wide range of other applications, as we will show in due course. PCA has been called, ’one of the most important results from applied linear algebra’[2] and perhaps its most common use is as the fi rst step in trying to analyse large data sets. Some of the other common applications include de-noising signals, blind source separation, and data compression.

SignalProcPCA.pdf

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