Reducing the influence of normalization on data classification

Principle Component Analysis (PCA) received a lot of attention over the past years and it is considered as a preprocessing method before many data mining models. PCA depends on the assumption that the input is normally distributed which is not true in many real life cases. On the other hand applying normalization on the input could change the structure of data and then affecting the outcome of multivariate analysis and calibration used in data mining. This paper introduces the effect of normalization methods before applying the conventional PCA. And It declares that the correlation PCA that uses the correlation matrix in PCA method could avoid such requirement. It proves that the correlation PCA leads to a better classification performance when the appropriate number of components is selected. The results also show that the resulted classification performance is independent on the normality of input.