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.-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.