文件名称:SignalProcPCA
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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.
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.
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