Abstract The performance assessments of existing data extraction algorithms mostly use variance contribution rate calculated by eigenvalues of raw data to measure the effect of feature extraction. However, variance contribution rate emphasizes the characteristic of eigenvalues of correlation matrix of the sample and it can not take information measuring into account. The extraction effect can be assessed from the angle of information theory by introducing Shannon information entropy into extraction algorithm, defining class probability and class information function and determining feature dimensions by calculating total information contribution rate. The theory are combined with factor analysis (FA) and FA feature extraction algorithm of information function is established. The extracting number of main factors is determined by information contribution rate. Finally, the efficiency of the theory is tested by cases.
2011, Vol. 24 
