Principal Component Analysis criteria

When calculating Principal Component Analysis, significant components can be selected on the basis of different criteria:

•AEC (Average Eigenvalue Criterion): all the components with eigenvalue greater than 1 are selected as significant components;
•CAEC (Corrected Average Eigenvalue Criterion): all the components with eigenvalue greater than 0.7 are selected as significant components;
•KP and KL are indices that take into account the correlation content of a set of multivariate data; further details on these indices can be found in the following references:

 

Todeschini R. (1997). Data correlation, number of significant principal components and shape of molecules. The K correlation index. Anal.Chim.Acta, 348, 419-430.

 

Todeschini R., Consonni V. and Maiocchi A. (1999). The K correlation index: theory development and its applications in chemometrics.  Chemometrics & Intell.Lab.Syst. , 46, 13-29.