Principal Component Analysis (PCA)

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Principal Component Analysis (PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations of those variables. PCA is thus often used as a technique for reducing dimensionality.
There are two primary reasons for using PCA:
  • Data Reduction
    PCA is most commonly used to condense the information contained in a large number of original variables into a smaller set of new composite variables or dimensions, at the same time ensuring a minimum loss of information.
  • Interpretation
    PCA can be used to discover important features of a large data set. It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not be concluded from ordinary examination of the data. PCA is typically used as an intermediate step in data analysis when the number of input variables is otherwise too large to perform useful analysis.
source: http://biit.cs.ut.ee/clustvis/




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