ABSTRACT
This chapter introduces a few essential techniques related to pattern recognition with the help of selected examples. Pattern recognition is a discipline whose goal is to classify objects in categories or classes and its wide applications range from machine vision, character recognition, computeraided diagnosis, speech recognition, and so on. Principal component analysis (PCA) is the workhorse of pattern recognition in chemometrics. Its aim is to reduce the dimensionality of a data-set while retaining as much variance as possible. It transforms the original variables in uncorrelated linear latent variables or components that can be extracted in order of decreasing content of variance. To avoid obtaining data which are not centered in the origin, it is generally recommended to perform a mean centering. PCA technique is extensively used while analyzing spectroscopic data due to the high correlated nature of the variable in exam. In cluster analysis, the groups are not known prior to the mathematical analysis.
